Washblog had a great headline to a short post that really got to me. It referred a horsesass.org post about Microsoft giving more money to Republicans than Dems. Well, any money at all it turns out.
Either way, here it is: "Linux, Apple, Open Source, Competition, Democrats." Contrasted with the text of the post: "Windows, Diebold, Proprietary, Monopoly, Republican."
Democrats are Firefox (or some other open source software).
Republicans are Explorer (or some other failed proprietary software).
In the Top Two realm, this would mean that Democrats would be ok with whomever chose to vote in their party ranks, even up to election day. Republicans would be a bit more stodgy about such an arrangement and get all bent out of shape.
The Democratic Party should be an open source party, because when you get down to it, all politics should be so called open source.
Good ideas are good ideas, and good candidates don't neccesarily get endorsed by the state chair.
Being involved in politics or your community (I don't think they're exclusive at all though) is good, it means your part of the solution. Not being involved is being part of the problem.
Open source software works because there are lots of people involved, making "whatever" work for the good of the everyone.
Politics works when everyone gets involved and everyone is heard. Its our democracy and it should be our Democratic Party.
Actually there are lots of people in the Washington State Democratic Party that work for Microsoft. And Microsoft donates to several key Washington State Democratic politicians.
Sometimes when I get on my high-horse about the party, I have a hard time getting across what I really mean. I wasn't trying to say that Microsoft or there employees aren't good Democrats, or can't be. I was tring to argue for a particular spirit of the Democratic Party, and that as a party we should be more like the open-source movement than the typical American corporation.


Growing up with CF, I was used to medicines and knew they were important for me to take regularly in order to stay healthy. My parents instilled this in me at a young age, so I am thankful that I had knowledgeable and caring parents - without them I would not have grown up. Now, that I'm an adult, it is my responsibility to take care of myself (although, they still check in regularly to make sure I'm doing what I'm supposed to!).
Unfortunately, there is a 3 year old little girl from West Virginia that did not have the same kind of parents that I did. Therefore, she sadly passed away last December after her mother failed to take her to doctor appointments and provide her with the necessary medicine for survival.
May this sweet little girl rest in peace and breathe easy!
Thanks to my Mom & Dad (and step mom & dad) for always taking care of me!! I wouldn't be here if it weren't for you guys!! Love you!!


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Home News Georgia House Speaker Optimistic That Voters Will Get to Decide on Gambling Expansion in 2022
Georgia House Speaker Optimistic That Voters Will Get to Decide on Gambling Expansion in 2022
House Speaker David Ralston said there's "an appetite" to get something done
A potential referendum would simply ask Georgians if they want to expand gambling
Bills to legalize casinos have been introduced in the past, but got nowhere
Two sports betting bills made headway in 2021, but politics got in the way
Dan Katz graduated with a BS in Commerce from the University of Virginia and an MBA from Emory University. He clearly uses neither of those degrees now, having been writing about the gambling industry (specializing in poker) since 2005. Prior to that, he was an IT and management consultant. He is an avid gamer, primarily on PC, and enjoys collecting retro video games and consoles; finding room to set them all up is his current challenge. Dan lives in Atlanta, Georgia with his wife and two children. You can get in touch with Dan at dan@vegasslotsonline.com.
Georgia House Speaker David Ralston thinks state residents might get the opportunity this year to decide if they want more gambling options beyond the lottery. [Image: Shutterstock.com]
Time to let voters be heard?
One of Georgia's highest-ranking politicians believes this could be the year that state residents get to decide if they want more gambling options. Speaking about gambling expansion, House Speaker David Ralston (R – Blue Ridge) told the media on Thursday: "There is an appetite this session that I haven't seen before to do something."
maybe it's time that we asked the question of Georgians whether they want to expand gaming"
"Maybe it's time that we asked the question of Georgians whether they want to expand gaming, and if they say yes, then we sit down and decide what form it will take, whether it's going to be sports betting, whether you do horses or destination resorts," he added.
Georgia is one of the most limited states in terms of gambling, with the lottery sitting as the lone legal, regulated option.
Ralston's idea is to put the decision to the voters, but it would only essentially be a "yes or no" question as to whether or not they want to added gambling options beyond the lottery. If voters give the hypothetical referendum the thumbs-up, state lawmakers would then decide where to go from there.
Some concrete plans, but no action
Some state lawmakers have introduced gambling legislation multiple times over the last few years, but nothing made it through Georgia's short legislative session. There have been several variations, from bills similar to Ralston's proposal that would allow voters to decide, to more detailed gambling expansion plans.
One such plan would have allowed for two "destination resort" casinos. One would have had to be in a county with at least 900,000 residents and a convention center, and developers would have had to invest at least $2bn into the project. So, an Atlanta-area casino. The other casino would have been in a county with between 250,000 and 900,000 residents.
Another casino plan would have divided the state into five or six regions, with one casino permitted in each and two in the largest, which would have been the Atlanta metropolitan area.
Though no casino bill was ever too much of a threat to get to the governor's desk, things did get serious enough where gambling companies started lining up to make their pitches. MGM proposed a $1bn facility in Atlanta. Speedway Motorsports, owner of the Atlanta Motor Speedway, partnered with Foxwoods owner Mashantucket Pequot Tribal Nation to develop a $1bn plan to develop a larger entertainment complex, including a casino, at the racetrack.
Sports betting came close in 2021
Most recently, two companion bills regarding sports betting came close to passing both chambers of the Georgia legislature last year. The would have put the decision to the people of the state and were supported, as one might expect, by Georgia's professional sports teams.
The bills passed the Senate, but stalled out in the House. Naturally, the most conservative Republicans were against gambling expansion, slowing things down, but an unrelated bill ultimately played a hand in killing it. Georgia took center stage in American politics for a while in 2021 when the Republican-controlled legislature passed a voting regulation bill, later signed by Governor Brian Kemp, that is widely seen as restricting voting rights and making it harder for people to vote, particularly people of color. Democrats and civil rights groups opposed it; Major League baseball even pulled the All-Star Game from Atlanta.
In protest of the voting regulation bill, House Democrats pulled their support for the sports betting bills and that was that.
the Georgia Lottery generated $1.2bn for education
Tax revenue from sports betting or other gambling expansion would be earmarked primarily for the HOPE Scholarship, which awards partial and full scholarship to students attending in-state colleges and universities. The HOPE Scholarship is funded by the lottery right now, but has become so popular that funds have been running low for years. In 2020, the Georgia Lottery generated $1.2bn for education, including pre-K programs.
Posted in: Legislation US News
Tags: gambling expansion Georgia
BGC Chief Warns Against Stricter Affordability Checks, Cites Study on Unlicensed Gambling
What Paul Scully's Speech Revealed About the UK Gambling Act White Paper
By Thomas Stead
By Thomas Stead - January 30, 2023 January 30, 2023

redpajama_set_name: RedPajamaCommonCrawl


Demystified: Why Do Wolves Howl?
— Today we present a Britannica Demystified on why wolves howl. Read on to learn more about how these majestic creatures communicate.
There's nothing quite so interesting as the social interactions in the wolf pack. Wolves live in packs of about 6 to 10 members. Pack formation is possible because wolves are highly social creatures that develop strong bonds with one another.
One of the ways in which wolves interact is through howling. A wolf's howl is a vocalization, which means that it's a sound produced in order to communicate. But what are they communicating, and with whom? It turns out that wolves howl to communicate their location to other pack members and to ward off rivaling packs from their territory. It's also been found that wolves will howl to their own pack members out of affection, as opposed to anxiety.
Wolf packs tend to claim large territories for themselves, especially if prey is scarce. These territories can be as large as 3,000 square km (1,200 square miles). Wolves may separate from their packs when hunting, so howling becomes an effective way to communicate about location. A wolf's howl can carry up to 16 km (10 miles) in the open tundra and a bit less in wooded areas.
Another sort of howl is an aggressive howl to other packs. It warns other packs or individual wolves in the area to stay away from the territory. A pack will also mark territory by using urine and feces.
A 2013 study added an additional reason behind wolves' howls: affection. The study found that wolves tend to howl more to a pack member that they have a strong connection with, meaning a close social connection. Scientists tested these wolves' saliva for cortisol, which is a stress hormone, and found that there were negligible results. It wasn't anxiety causing these wolves to howl for each other. Rather, it may have been affection or another emotion not driven by anxiety.
What are "coywolves," and why should you care about them?
There's a certain brand of annihilating ecological plunder that, in the public imagination, has been somewhat checked in the last several decades. Yes, clear-cutting, strip mining, and the dumping of untreated industrial byproducts still occur, but surely at much reduced rates, at least in the developed world, or so I imagine the casual observer of the state of the environment thinking. I sometimes find myself lapsing into similar complacency, situated as I am on the Chicago shores of Lake Michigan. Though that body of water is hardly untainted, it at least doesn't look hideously polluted most of the time. No scum of waste apocalyptically ablaze on its waves, no odd chemical tint to the currents (at least none that I've seen).
Certainly, we find ourselves believing, the orthodoxy of the Western world has curved toward conservation. Even if scores of battles remain to be fought on that front, the ramparts are manned and right is on our side. Cecil the lion should not have died. Elephants should not be killed for their ivory. Whaling and seal clubbing are ethically abhorrent practices. Entire species should not be hunted to extinction. Deforestation is bad. These are truisms to devoted advocates and armchair environmentalists alike and woefully inadequate though it may be, at least in some quarters, legislation and enforcement exist to hold back the tide of wholesale destruction.
Yet a pillage continues to occur, even in the West, that equals, if not exceeds, the depredation of the world's rainforests, the slaughter of its terrestrial megafauna, and the heedless plunder of its mineral wealth. And the bulwarks against it are frail, where they exist at all. Cleverly concealed in the ocean depths, a holocaust is occurring. The more palatable denizens of the sea are already overfished in many areas of the world. But these "target species"—the species fishing operations specifically hunt—constitute only a portion of the casualties.
This week's Take Action Thursday urges support for legislation to end the military's use of live animals for combat and medical training exercises.
S 587 and HR 1095, the Battlefield Excellence through Superior Training Practices Act or "BEST Practices Act," seek to ban the use of animals for medical and combat training in the military by 2020. The U.S. Department of Defense uses more than 8,500 live animals each year to train medics and physicians on methods of responding to battlefield injury. While the military has already made some great strides towards using more humane and human-relevant training options, this bill would require all branches of the military to use state-of-the-art simulators and other non-animal methods, instead of relying on goats and pigs to provide medical and trauma training.
The military base is already the subject of much dissention in Japan, both within the community around the proposed site as well as between the local Okinawa government and the central government in Tokyo. While local authorities in Okinawa refuse to grant the construction permit needed to move forward with the base, the U.S. lawsuit is awaiting a decision of the U.S. Court of Appeals for the Ninth Circuit.
In appreciation of the peaceful and endangered manatee, and in recognition of Manatee Awareness Month, Advocacy for Animals presents this article on manatees from the Encyclopædia Britannica. Manatees have been listed as endangered since 1967 and still face serious dangers, including vulnerability to cold, collisions with boats (which cause about a fourth of all manatee deaths annually), and red tides, blooms of algae that release substances toxic to many sea animals. We hope you enjoy learning more about the manatee and are inspired to help protect them. For more information on what you can do to help, visit groups working for manatee welfare, such as Defenders of Wildlife and Save the Manatee Club.
(genus Trichechus), any of three species of large, slow aquaticmammals found along tropical and subtropical Atlantic coasts and associated inland waters. Dull gray, blackish, or brown in colour, all three manatee species have stout, tapered bodies ending in a flat, rounded tail used for forward propulsion. The forelimbs are modified into flippers; there are no hind limbs.
The Florida manatee (T. manatus latirostris), which is also found seasonally in the waters of nearby states, is one subspecies of the West Indian manatee (T. manatus). The other subspecies lives in nearshore waters, lagoons, estuaries, and rivers of eastern Mexico, down the Central American coast, and across northern South America. It also occurs around the Greater Antilles islands of the Caribbean—hence its common name, the Antillean manatee (T. manatus manatus).
The Amazonian manatee (T. inunguis) inhabits the Amazon River and associated drainage areas, including seasonally inundated forests. This species lives only in fresh water and can be found far inland through Brazil to Ecuador, Peru, and Colombia. The West African manatee (T. senegalensis), found in coastal areas and slow-moving rivers from Senegal to Angola, also ranges far inland in some rivers.


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Il palazzo Comunale o palazzo del Comune è di solito l'edificio in cui ha sede (o ha avuto sede) l'ente amministrativo del Comune.
 Brasile
 Palazzo Comunale – Porto Alegre

 Croazia
 Palazzo Comunale – Pola
 Palazzo Comunale – Rovigno

 Italia
 Palazzo dell'Arengo o Palazzo Comunale – Ascoli Piceno
 Palazzo Comunale – Alcamo in provincia di Trapani
 Palazzo Comunale – Assisi in provincia di Perugia
 Palazzo Comunale – Bobbio in provincia di Piacenza
 Palazzo d'Accursio o Palazzo Comunale – Bologna
 Palazzo Comunale – Borgo San Lorenzo in provincia di Firenze
 Palazzo Comunale – Castel Goffredo in provincia di Mantova
 Palazzo Comunale – Cervia in provincia di Ravenna
 Palazzo Comunale – Cesena in provincia di Forlì-Cesena
 Palazzo Comunale – Cingoli in provincia di Macerata
 Palazzo Comunale – Crema in provincia di Cremona
 Palazzo Comunale – Cremona
 Palazzo Comunale – Crotone
 Palazzo Comunale – Favara in provincia di Agrigento
 Palazzo Comunale – Fidenza in provincia di Parma
 Palazzo Comunale – Filottrano in provincia di Ancona
 Palazzo Comunale – Foligno in provincia di Perugia
 Palazzo Comunale – Forlì in provincia di Forlì-Cesena
 Palazzo Comunale – Gavorrano in provincia di Grosseto
 Palazzo Comunale – Grammichele in città metropolitana di Catania
 Palazzo Milesi o Palazzo Comunale – Gromo in provincia di Bergamo
 Palazzo Comunale – Grosseto
 Palazzo Comunale – Jesi
 Palazzo Comunale – Langhirano in provincia di Parma
 Palazzo Comunale – Lastra a Signa nella città metropolitana di Firenze
 Palazzo Comunale – Licodia Eubea nella città metropolitana di Catania
 Palazzo Comunale – Livorno
 Palazzo Comunale – Marino in provincia di Roma
 Palazzo Comunale – Massa Marittima in provincia di Grosseto
 Palazzo Zanca o Palazzo Comunale – Messina
 Palazzo Comunale – Mirandola in provincia di Modena
 Palazzo Comunale – Modena
 Palazzo Comunale – Montecatini Terme in provincia di Pistoia
 Palazzo Comunale – Montepulciano in provincia di Siena
 Palazzo Comunale – Monterotondo Marittimo in provincia di Grosseto
 Palazzo Comunale – Montieri in provincia di Grosseto
 Palazzo Comunale – Nepi in provincia di Viterbo
 Palazzo Comunale – Nocera Inferiore in provincia di Salerno
 Palazzo Comunale – Norcia in provincia di Perugia
 Palazzo Comunale – Osimo in provincia di Ancona
 Palazzo Comunale – Padova
 Palazzo del Comune – Parma
 Palazzo Mezzabarba o Palazzo Comunale – Pavia
 Palazzo Comunale – Piacenza
 Palazzo Comunale – Pienza in provincia di Siena
 Palazzo Comunale – Piombino in provincia di Livorno
 Palazzo degli Anziani o Palazzo Comunale – Pistoia
 Palazzo Comunale – Prato
 Palazzo Comunale – Priverno in provincia di Latina
 Palazzo Comunale – Pordenone
 Palazzo del Comune – Reggio nell'Emilia
 Palazzo Comunale – Rieti
 Palazzo Avitaja o Palazzo Comunale – Ruvo di Puglia nella città metropolitana di Bari
 Palazzo Comunale – Sabaudia in provincia di Latina
 Palazzo Comunale – San Gimignano in provincia di Siena
 Palazzo Comunale – San Miniato in provincia di Pisa
 Palazzo Comunale – San Severino Marche in provincia di Macerata
 Palazzo Comunale – Sant'Ambrogio di Torino in provincia di Torino
 Palazzo Comunale – Scarlino in provincia di Grosseto
 Palazzo Comunale – Senigallia
 Palazzo Pubblico o Palazzo Comunale – Siena
 Palazzo Comunale – Sovana (frazione di Sorano in provincia di Grosseto
 Palazzo Comunale – Spoleto in provincia di Perugia
 Palazzo Comunale – Strambino nella città metropolitana di Torino
 Palazzo Comunale – Torrita di Siena in provincia di Siena
 Palazzo Comunale – Ulassai in provincia di Nuoro
 Palazzo Comunale – Urbania in provincia di Pesaro e Urbino
 Palazzo Comunale – Urbino in provincia di Pesaro e Urbino

Pagine correlate
 Palazzo Civico
 Palazzo del Podestà
 Palazzo Municipale
 Palazzo Pretorio

redpajama_set_name: RedPajamaWikipedia


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Budget's big bank levy could open the door for fintech lenders
Kelly Emmerton
In the 2017 Budget announcement, Treasurer Scott Morrison announced a 0.06% annual levy on key funding sources for Australia's major banks - a cost that could be passed on to Aussie borrowers and may drive them toward alternate lending sources.
The levy will apply to funding sources such as corporate bonds and large deposits and will only affect banks with debts above $100 billion - effectively limiting it to big players the Commonwealth Bank, Westpac, NAB, ANZ and Macquarie.
"This represents a fair contribution to the community from our major banks, is consistent with other advanced countries and helps foster competition from smaller banks," according to budget papers.
RELATED: Big banks total half year profits crack $15 billion
If it passes Parliament, the new levy will be introduced on 1 July, and is expected to cost the banks around $6.2 billion over the next four years. At $1.5 billion each year, that's less than 5% of the major banks combined $30 billion annual profits.
But because the major banks control such a large portion of the Australian loan market, there are concerns that that cost may wind up being passed back down to customers in the form of further rate hikes.
Westpac Group CEO, Brian Hartzer, said, "The Australian banks are already the largest taxpayers, with Westpac the country's second largest taxpayer. Westpac already pays over 30% of its profits in tax and this will now increase even further."
He said that ultimately, the cost of the new tax would be "borne by shareholders, borrowers, depositors, and employees."
For Aussies looking for personal loans, one way to avoid footing the bill for the big bank levy is to look outside the major banks when borrowing, and consider smaller non-bank, online or peer-to-peer lenders.
RELATED: Peer to peer lending in Australia explained
Peer-to-peer lender RateSetter welcomed the Budget reforms, with CEO Daniel Foggo saying fintech companies could play an important role in driving the kind of competition the government is promoting.
"In requiring big banks to give consumers greater control over their own data, the government has stood up to the banking sector lobbyists and shown it's serious about boosting competition in financial services in Australia," he said.
"In simple terms, this measure will help break down the huge advantage big banks have over innovative new entrants and make it significantly easier and faster for consumers and small businesses to switch to providers like RateSetter who can offer a much better deal."
If you're looking for a personal loan, be sure to check out our peer-to-peer comparison table and crunch the numbers with our personal loan calculator to see how much you could save by opting for an alternative to the big banks.
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Pet friendly college towns are a great weekend getaway for your whole family any time of the year. As a matter of fact, the phrase "pet friendly college towns" is a redundancy. It's like saying "egotistical politician" or "greedy Wells Fargo." You get the drift. That's because ALL college towns are pet friendly. And, better still, you don't need to have family members or friends in college as an excuse to visit.
At Petswelcome, we like to think outside the box and utilize places for other than what they were intended for. We take great joy in corrupting their original purpose with our twisted world vision of turning everything into a dog party. In the past, we've recommended patronizing food trucks and beer gardens because they are a great way to dine out with your dog even though that is obviously is not their intended purpose. Or staying at Extended Stay hotels with your pet even if it's just for a day or two because they offer many of the amenities pet owners need no matter the length of stay. Now that's what we call in the trade SPO (Subversive Pet Opportunities). Are you seeing a pattern here?
College towns are awesome because they have so much to offer, including a variety of both cheap restaurants (because students are poor) and nice ones (because their parents are not). They also have a laidback atmosphere, outdoor music (because young people like music and the outdoors), sporting events, art shows, great bookstores, coffee shops, bars, beautiful campuses with quads to hang out in, readings, and so much more. Oh, and did I mention a LOT of really smart, creative and interesting people live in college towns? 'Nuf said.
Then of course, there are the dogs. And cats. And birds. And ferrets. And every other type of pet because most young people (am I sounding old?) love animals of all types. The great thing about college towns is that you will only get a double-take if you don't have an animal with you (or you're wearing a leisure suit). So, pack your bags and be prepared to be enlightened.
Home to Brown University and the Rhode Island School of Design (RISD), Providence is a beautiful city with plenty of pet friendly venues including the Bell Street Dog Park, Waterman Street Dog Park, as well as great eateries like Constantino's Venda Bar & Ristorante and Nick-a-Nees, a must-visit dive bar.
Brown University also is a leader in dog research, including a program that designs robotic dogs to help older adults—especially those suffering from dementia—accomplish basic tasks like taking their medications. There is also one that studies dog sneezing. Really.
Pet-friendly lodging is available at The Hotel Providence, the Omni Providence Hotel and the Providence Marriott Downtown.
Colgate University, which is located in Hamilton, has a beautiful 575- acre campus that is a great place to stroll with your favorite animal. If you happen to be a student there—and a pet lover—you can join Colgate is Pet Pals and volunteer at the Wanderer's Rest Humane Society and the Spring Farm Cares Animal & Nature Society to help less fortunate animals get the love, care and attention they so richly deserve.
Off campus, Hamilton has a lovely village green as well as great pet-friendly places to eat and imbibe your favorite aperitifs. The Inn at Colgate has a front patio that welcomes pets, as does the Good Nature Farm Brewery & Tap Room which sources ingredients for all its beers from New York State famers. It has a beer garden and a great selection of food, including Smash Burgers, Sammiches and Woodfired Pizza.
In addition to the Inn at Colgate, you can find pet-friendly hotels in nearby Canastota at the Days Inn Canasota/Syracuseand the Super 8 by Wyndham Oneida Verona.
Swarthmore is home to, you guessed it, Swarthmore College which was founded in 1864 and is one of the oldest co-ed colleges in the country. The Scott Arboretum of Swarthmore College is a must-see destination where you and your pooch can ramble over 350 acres that contain more than 4,000 plant varieties. And, once you've built up a healthy appetite (and thirst), you can hit the pet-friendly Ariano Restaurant & Bar in Media, PA, as well as Pinnochio's Restaurant & Beer Garden to Go that has thousands of beers from over 200 breweries.
Pet friendly lodging can be found in nearby Essington, PA, at the La Quinta Inn & Suites or the Wyndham Garden Philadelphia Airport, among others.
Located in the beautiful Berkshire Mountains, Williams College was founded in 1755. It boasts a beautiful 450-acre campus whose design is the work of the Olmstead brothers. Their father, Frederick Law Olmstead, was the great American landscape architect who designed the grounds of the US Capitol as well as Central Park in New York City, and the Williams campus reflects such a distinguished lineage.
Great hiking can be had in the many pet-friendly trails that run in and around Williams, including the Berlin Road Trail System that features a great variety of flora and fauna as well as interpretive signs along the trail. When it comes to pet- friendly eating and drinking, you can stop by the pet-friendly patio at The '6 House Pub or the Purple Pub which also has a pet-friendly patio and offers great pub fare such as Extreme Pub Nachos or the Sister Havana Cuban Sandwich.
Choices for pet-friendly lodgings include the Clover Hill Farm, a beautiful country inn, as well the Williamstown Motel and the Green Valley Motel.
To find more intelligent suggestions of great places to visit, be sure to check out Petswelcome's list of the Top 25 Pet Friendly College Towns in the US.


Our Ring of Elysium Hack has been updated for the STEAM version of the game.
The hack is available to our MasterPackage & Radar-Package subscribers.
You will be able to see all Players, Vehicles, Items, Weapons, Ammo, Boxes, ... on the 3D/2D Radar.
And you can use our Bone Aimbot (MasterPackage only) to make sure you are the winner.


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, aussi connu sous le titre , est un shōjo manga de Tomoko Hayakawa. Yamato Nadeshiko désigne l'icône de la femme parfaite. Il est prépublié entre 2000 et 2015 dans le magazine Bessatsu Friend et est compilé en un total de trente-six tomes. La version française est publiée par Pika Edition, et vingt-deux tomes sont disponibles en .

Une adaptation en série télévisée d'animation produite par le studio Nippon Animation a été diffusée sur TV Tokyo et Aichi Television Broadcasting entre le  et le . Un drama intitulé  a également été réalisé en 2010. Il comporte dix épisodes d'environ 45 minutes chacun, à l'exception du premier qui dure presque une heure. On y retrouve les mêmes protagonistes, mais ils ont alors 21 ans au lieu de 15.

Histoire
Le confort d'une villa gratuit ou le loyer triplé... Il suffira aux quatre adolescents qui la louent actuellement d'héberger Sunako, la nièce de la propriétaire, et d'en faire une lady avant le retour de sa tante. Simple... en apparence.

En effet, la jeune Sunako se révèle être quelqu'un d'apparence plutôt négligée. Fascinée par le morbide et le gore (tête de mort, squelette, films d'horreur), les gens doutent souvent de son appartenance à l'espèce humaine : elle a renié toute forme de beauté et de communication avec le reste du monde à la suite d'une déception amoureuse. Hantée par les mots de celui qu'elle aimait, la beauté de ses congénères la révulse ; elle dit être "éblouie" et ne pas supporter leur lumière et saigne du nez à chaque fois qu'elle regarde une belle personne trop longtemps. En ignorant sa propre image, Sunako pense échapper à la comparaison avec les autres jeunes filles.

Les quatre jeunes hommes - des bishônen extrêmement populaires et constamment assaillis par des groupies gloussantes - feront tout ce qui est en leur pouvoir pour transformer Sunako en véritable femme du monde.

Personnages

Âge : 15 ans
Nourriture favorite: Chocolat
Aime : tout ce qui a trait à la culture gothique
Personnage central, en première année au lycée. Elle vient s'installer chez sa tante qui loue déjà des chambres à quatre garçons. À la suite d'une mauvaise expérience, elle a développé un complexe face à son apparence. Passionnée de films d'horreur, pourvu qu'ils soient les plus gores possibles, têtes de mort et autres symboles gothiques. Elle ne supporte ni la lumière, ni la beauté et encore moins ses quatre bishōnen de colocataires, qu'elle juge trop "éblouissants". Ses seuls amis se réduisent à des squelettes et figurines d'anatomie, Joséphine, Akira et Hiroshi entre autres. Son apparence varie au gré des situations entre SD (Super Deformed) et mode "beauté fatale". Dans le drama, elle est très attachée à son cousin (fils de la propriétaire), Takeru. Toujours dans le drama, elle obtient des pouvoirs étranges chaque fois qu'elle se met en colère ce qui n'arrive que lorsque Kyohei l'appelle "Busunako" (fille moche).

Âge : 15 ans
Taille :
Poids :
Particularité : Bon combattant
Nourriture favorite : les fraises, les crevettes frites
Le plus intempestif des quatre garçons. Très bon combattant, ayant une passion toute particulière sur tout ce qui touche à la nourriture et à l'argent. Son histoire personnelle illustre particulièrement bien la thèse de "la beauté comme une malédiction". Il devient de plus en plus complice avec Sunako et semble éprouver des sentiments très fort envers elle : il pique une crise de jalousie lorsqu'elle lui déclare qu'il compte moins pour elle que sa poupée en plastique, Hiroshi. Ils se sont même embrassés : Sunako fut possédée par un esprit et pour la faire revenir l'embrassa (dans le manga), dans l'animé, ils s'embrassent lors d'une partie de ping-pong où ils se percutent violemment. Ils s'embrassent également dans le drama après que Sunako ait été intoxiquée par des champignons car il croit que c'est la seule manière de la libérer des effets du poison. Sa mère, ayant marre que son fils se fasse harceler, l'abandonna.

Âge : 15 ans
Taille :
Poids:
Le plus intellectuel des quatre garçons qui est aux dires de l'auteur le plus bishōnen. Sa petite amie est Noi. Ses parents sont de riches bourgeois. Ils l'ont placé dans la maison de la propriétaire car ayant reçu une éducation très stricte, il était devenu misanthrope. Sa famille est réputée pour ses compositions florales.

 Yukinojo "Yuki" Toyama
Âge : 15 ans
Taille :
Poids :
Le plus androgyne des quatre garçons. Souvent utilisé pour interpréter des rôles féminins, il aime les animaux et a une petite amie prénommée Machiko. Son père est prolétaire.

 Ranmaru Morii
Âge : 15 ans
Taille :
Poids :
Particularité : playboy
Le plus tombeur des quatre garçons, sortant avec différentes femmes plus âgées et souvent mariées. Il semble tomber amoureux de sa "fiancée" au fil des épisodes. Ses parents sont propriétaires d'une banque et à force de le gâter, Ranmaru finit par devenir égoïste. C'était la raison de sa présence chez la tante de Sunako.

 Naie "Noi" Kasahara
Âge : 15 ans
Aime :  Takenaga Oda
Une amie des quatre garçons. Fille très populaire, amoureuse transie de Takenaga.

 Kikunoi Tamao
Âge : 15 ans
Aime :  Ranmaru
Fiancée de Ranmaru. C'est "l'incarnation d'une héritière noble". Elle est amie avec Sunako et Noi.

 La propriétaire
Tante de Sunako. Belle, riche et veuve excentrique, elle fait un marché avec les quatre garçons qui doivent transformer Sunako en vraie lady.

Manga

La série est publiée entre 2000 et 2015 dans le magazine Bessatsu Friend de l'éditeur Kōdansha. Le premier volume relié est sorti le  et le trente-sixième et dernier est publié le . La version française est publiée par Pika Édition. Il est publié en anglais à Singapour sous le titre My Fair Lady par Chuang Yi, et en Amérique du Nord sous le titre The Wallflower par Del Rey Manga. Un autre titre utilisé en anglais est Sunako of Many Forms.

Anime
L'anime de  correspond au déroulement du manga, respectant ainsi les mêmes histoires et le caractère des personnages. L'anime est composé de 25 épisodes.

Fiche technique
 Réalisateur : Shinichi Watanabe
 Musique : Hiromi Mizutani, Yasuharu Takanashi
 Character Design : Yasuko Sakuma
 Directeur artistique : Yumi Kudou
 Directeur de l'animation : Yasuko Sakuma
 Design des Mecha : Tetsuro Aoki
 Directeur de la Photographie : Jun-Ichi Takeda
 Photographie : Takashi Yamaguchi, Tomohiro Urabayashi
 Producteur Assistant : Masashi Takatori
 Peinture digitale : Koyuri Takagi, Miyoko Yoshida, Naomi Takahashi, Toshiko Iwakiri
 Édition : Shinichi Natori
 Producteurs : Jun Togawa, Shinsaku Hatta, Takashi Watanabe
 Directeur du son : Hiroyuki Hayase

Distribution
 Sachiko Takaguchi : Sunako Nakahara
 Shōtarō Morikubo : Kyohei Takano
 Tomokazu Sugita : Takenaga Oda
 Yūya Yamauchi : Yukinojo Tōyama
 Hirofumi Nojima : Ranmaru Morii
 Mai Nakahara : Noi Kasahara

Liste des épisodes

Musique
Tomoko Hayakawa, l'auteur du manga, est une fan du chanteur japonais Kiyoharu. L'opening et l'ending ont donc tous deux été interprétés par le même artiste.
 Générique d'ouverture : Slow de Kiyoharu
 Générique de fin : Carnation de Kiyoharu

Références

Liens externes
  du manga
  de l'anime
  du drama

Manga des années 2000
Manga paru dans le Bessatsu Friend
Manga se déroulant dans un milieu scolaire
Série d'animation japonaise
Série manga
Animation réalisée par Nippon Animation


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text:
\section{Introduction}
Since universities are prestigious places of higher education, students' retention in these universities is a matter of high concern \cite{aud2013condition}. It has been found that most of the students' drop-out from the universities during their first year is due to lack of proper support in undergraduate courses  \cite{callender2009part} \cite{macdonald1992meeting}. Due to this reason, the first year of the undergraduate student is referred as a ``make or break'' year. Without getting any support on the course domain and its complexity, it may demotivate a student and can be the cause to withdraw the course. There is a great need to develop an appropriate solution to assist students retention at higher education institutions. Early grade prediction is one of the solutions that have a tendency to monitor students' progress in the degree courses at the University and will lead to improving the students' learning process based on predicted grades.

Using machine learning with Educational Data Mining (EDM) can improve the learning process of students. Different models can be developed to predict students' grades in the enrolled courses, which provide valuable information to facilitate students' retention in those courses. This information can be used to early identify students at-risk based on which a system can suggest the instructors to provide special attention to those students \cite{iraji2012students}. This information can also help in predicting the students' grades in different courses to monitor their performance in a better way that can enhance the students' retention rate of the universities.

Several research studies have been conducted to assess and predict students' performance in the universities. In  \cite{iqbal2016admission}, we analyzed various existing international studies and examined the admission criterion of ITU to found which admission criterion factor can predict the GPA in the first semester at the undergraduate level. From the results, we found that Higher Secondary School Certificate (HSSC) performance and entry test performance are the most significant factors in predicting academic success of the students in the first semester at university. In this study, we are further extending this research and examining the effectiveness of the performance of students of ITU in enrolled courses using machine learning techniques.

In this study, we applied various techniques (CF, SVD, NMF, and RBM) on the real-world data of ITU students. The CF techniques are one of the most popular techniques for predicting students' performance \cite{sarwar1998using}, which works by discovering similar characteristics of users and items in the database; CF, however, does not provide an accurate prediction for a sparse database. The SVD technique makes better predictions as compared to CF algorithms for sparse databases by capturing the hidden latent features in the dataset while avoiding overfitting \cite{berry1995using}. The NMF technique allows meaningful interpretations of the possible hidden features compared to other dimensionality reduction algorithms such as SVD \cite{golub2012matrix}. Finally, RBM can also be used for collaborative filtering and was used for collaborative filtering during the Netflix competition \cite{salakhutdinov2007restricted}. \cite{toscher2010collaborative} tried to use RBM on the KDD Cup dataset and got promising results.

The contributions of this paper are:

\begin{enumerate}
  \item We systematically reviewed the literature about grade/GPA prediction and comprehensively presented them.
  \item We analyzed a real world data collected from 225 undergraduate students of Electrical Engineering Department at ITU.
  \item We evaluated state of the art machine learning techniques (CF, SVD, NMF, and RBM) in predicting the performance of ITU students.
  \item We proposed a feedback model to calculate the student's knowledge for particular course domain and provide feedback if the student needs to put more effort in that course based on the predicted GPA.
  \item We proposed a fitting procedure for hidden Markov model to determine the student performance in a particular course with utilizing the knowledge of course domain.
\end{enumerate}

Rest of the paper is organized as follows. In Section 2, we will describe related work proposed in the literature. Different machine learning techniques that can be utilized to predict students' GPA are briefly outlined in section 3. The methodology of the study for this paper and the performance of the ITU students in different courses are described in Section 4. We present the results and findings of our study in Section 5. We described the insights that hold for our study in Section 6. We highlight some limitations of this study in Section 7. Finally, we conclude the paper in Section 8.

\section{Related Work}
Numerous research studies have been conducted to predict students' academic performance either to facilitate degree planning or to determine students at risk.
\subsection{Matrix Factorization}
\cite{thai2011factorization} proposed matrix factorization models for predicting student performance of Algebra and Bridge to Algebra courses. The factorization techniques are useful in case of sparse data and absence of students' background knowledge and tasks. They split the data into trainset and testset. The data represents the log files of interactions between students and computer aided tutoring systems. \cite{thai2011matrix} extended the research and used tensor-based factorization to predict student success. They formulated the problem of predicting student performance as a recommender system problem and proposed tensor-based factorization techniques to add the temporal effect of student performance. The system saves success/failure logs of students on exercises as they interact with the system.

\subsection{Personalized Multi-Linear Regression models (PLMR)}
Grade prediction accuracy using Matrix Factorization (MF) method degrades when dealing with small sample sizes. \cite{elbadrawy2016predicting} investigated different recommender system techniques to accurately predict the students' next term course grades as well as within the class assessment performance of George Mason University (GMU), University of Minnesota (UMN) and Stanford University (SU). Their study revealed that both Personalized Multi-Linear Regression models (PLMR) and advance Matrix Factorization (MF) techniques could predict next term grades with lower error rate than traditional methods. PLMR was also useful for predicting grades on assessments within a traditional class or online course by incorporating features captured through students' interaction with LMS and MOOC server logs.

\subsection{Regression and Classification Models}
The final grade prediction based on the limited initial data of students and courses is a challenging task because, at the beginning of undergraduate studies, most of the students are motivated and perform well in the first semester but as the time passed there might be a decrease in motivation and performance of the students. \cite{meier2016predicting} proposed an algorithm to predict the final grade of an individual student when the expected accuracy of the prediction is sufficient. The algorithm can be used in both regression and classification settings to predict students' performance in a course and classify them into two groups (the student who perform well and the student who perform poorly). Their study showed that in-class exams were better predictors of the overall performance of a student than the homework assignment. The study also demonstrated that timely prediction of the performance of each student would allow instructors to intervene accordingly. \cite{zimmermann2015model} considered regression models in combination with variable selection and variable aggregation approach to predict the performance of graduate students and their aggregates. They have used a dataset of 171 students from Eidgenössische Technische Hochschule (ETH) Zürich, Switzerland. According to their findings, the undergraduate performance of the students could explain 54\% of the variance in graduate-level performance. By analyzing the structure of the undergraduate program, they assessed a set of students' abilities. Their results can be used as a methodological basis for deriving principle guidelines for admissions committees.

\begin{comment}
\subsection{Learning Management System (LMS)}
Learning Management Systems (LMSs), for example, Moodle\footnote{\url{http://moodle.org}} provide students with online access to course content and to communicate and collaborate with instructors and peers. The data collected from LMS can be used to track students' involvement in the studies and to predict their future academic performance \cite{romero2008data}. Currently, Massive Open Online Courses (MOOCs) are the popular low-cost technological solution to deliver distance learning education to students across the world \cite{pappano2012year}. Many approaches have been used to forecast the performance of the students. \cite{elbadrawy2016domain} described how the student and course academic features determined the enrollment patterns by using academic features to determine student and course groups at various levels of granularity. Their study also showed that incorporating the features-based groups into the various methods lead to better grade predictions and course rankings.
\end{comment}

\subsection{Multilayer Perceptron Neural Network}
Educational Data Mining utilizes data mining techniques to discover novel knowledge originating in educational settings \cite{baker2009state}. EDM can be used for decision making in refining repetitive curricula and admission criteria of educational institutions \cite{calders2012introduction}. \cite{saarela2015analysing} applied the EDM approach to analyze the effects of core Computer Science courses and provide novel information for refining repetitive curricula to enhance the success rate of the students. They utilized the historical log file of all the students of the Department of Mathematical Information Technology (DMIT) at the University of Jyväskylä in Finland. They analyzed patterns observed in the historical log file from the student database for enhanced profiling of the core courses and the indication of study skills that support timely and successful graduation. They trained multilayer perceptron neural network model with cross-validation to demonstrate the constructed nonlinear regression model. In their study, they found that the general learning capabilities can better predict the students' success than specific IT skills.

\subsection{Factorization Machines (FM)}
Next term grade prediction methods are developed to predict the grades that a student will obtain in the courses for the next term. \cite{sweeney2015next} developed a system for predicting students' grades using simple baselines and MF-based methods for the dataset of George Mason University (GMU). Their study showed that Factorization Machines (FM) model achieved the lowest prediction error and can be used to predict both cold-start and non-cold-start predictions accurately. In subsequent studies, \cite{sweeney2016next} explored a variety of methods that leverage content features. They used FM, Random Forests (RF), and the Personalized Multi-Linear Regression (PMLR) models to learn patterns from historical transcript data of students along with additional information about the courses and the instructors teaching them. Their study showed that hybrid FM-RF and the PMLR models achieved the lowest prediction error and could be used to predict grades for both new and returning students.

\subsection{Dropout Early Warning System (DEWS)}
Dropout early warning systems help higher education institutions to identify students at risk, and to identify interventions that may help to increase the student retention rate of the institutes. \cite{knowles2015needles} utilized the Wisconsin DEWS approach to predict the student dropout risk. They introduced flexible series of DEWS software modules that can adapt to new data, new algorithms, and new outcome variables to predict the dropout risk as well as impute key predictors.

\begin{comment}
In subsequent studies, \cite{xumachine} developed a novel machine learning method for predicting student performance in degree programs. Their proposed method addresses the diversity of students' backgrounds and selected courses to make accurate predictions. They further developed an ensemble based progressive prediction architecture that incorporates students' evolving performance into the prediction.
\end{comment}

\subsection{Hidden Markov Model and Bayesian Knowledge Tracing}
Hidden Markov model has been used widely to model student learning. \cite{van2013properties} investigated solutions of hidden Markov model and concluded that the utilization of a maximum likelihood test should be the preferred method for finding parameter values for the hidden Markov Model. \cite{hawkins2014learning} in a separate study developed and analyzed a new fitting procedure for Bayesian Knowledge Tracing and concluded that empirical probabilities had the comparable predictive accuracy to that of expectation maximization.

In Table \ref{table:literature-review}, we have systematically summarized the studies that are related to our study in a comprehensive way to present a big picture of literature. Our work is related to grade prediction systems, recommender systems, and early warning systems within the context of education. In our study, the approach is to use machine learning techniques to predict course grades of students. We used the state of the art techniques that are described and implemented in this section to do a comparative analysis of different techniques that can predict students' GPA in registered courses. We also develop a model that can be used in a tutoring system indicating the weak students in the course to the instructor and providing early warnings to the student if he/she needs to work hard to complete the course.

\begin{table}[!h]
\caption{Systematic Literature Review}
\label{table:literature-review}
\scriptsize\begin{tabularx}{\textwidth}{XXXXX}
\hline
Study & Study Purpose & Dataset & Methods / Techniques & Relevant Findings \\ \hline
\cite{thai2011matrix} & Factorization approaches to predict student performance. & Two real-world datasets from KDD Cup 2010. & Matrix Factorization & MF technique can take slip and guess factors to predict performance.\\
\hline

\cite{thai2011factorization} &
Matrix factorization models for predicting student performance. & Two real-world datasets from KDD Cup 2010. & Matrix Factorization and Tensor based Factorization & MF techniques are useful for sparse data to predict the performance. \\
\hline

\cite{hawkins2014learning} & Analyze a new fitting procedure for Bayesian Knowledge Tracing. & 1,579 students working on 67 skill-builder problem sets. & Bayesian Knowledge Tracing & Probabilities have accuracy to Expectation Maximization.\\
\hline

\cite{zimmermann2015model} & Predict graduate performance using undergraduate performance. & 171 students data from ETH Zurich. & Regression models. & Third year GPA of undergraduate can predict graduate performance.\\
\hline

\cite{saarela2015analysing} & Analysing students performance using sparse dataset. & Students data of DMIT 2009 - 2013. & Multilayer perceptron neural network & General learning capabilities can predict the students' success.\\
\hline

\cite{sweeney2015next} & Predict students' course grades for the next enrollment term. & 33000 GMU students data of fall 2014. & Factorization Machine & FM model can predict performance with lower prediction error.\\
\hline

\cite{knowles2015needles} & Build a dropout early warning system. & 2006-07 grade 7 cohorts. & Dropout Early Warning Systems (DEWS). & DEWS can predict the dropout risk as well as impute key predictors.\\
\hline

\cite{elbadrawy2016domain} & Investigate the student and course academic features. & 1,700,000 grades from the University of Minnesota. & Collaborative Filtering and Matrix Factorization & Features-based groups make better grade predictions. \\
\hline

\cite{elbadrawy2016predicting} & Predict next term course grades and within-class assessment performance & 30,754 GMU, 14,505 UMN and 13,130 SU students' data. & Personalized Multi-Linear Regression models (PLMR) & PLMR and MF can predict next term grades with lower error. \\
\hline

\cite{sweeney2016next} & Predict students' grades in the courses they will enroll in the next term. & 33000 GMU students data. & Hybrid FM-RF and the PMLR models & Hybrid FM-RF and PMLR methods can predict students' grades. \\
\hline

\cite{meier2016predicting} & Predict grades of individual students in traditional classrooms. & 700 UCLA undergraduate students data. & Regression and classification & In-class evaluations enables timely identification of weak students.\\
\hline

\cite{xumachine} & Machine learning method for predicting student performance. & 1169 UCLA undergraduate students data. & Latent factor method based on course clustering & Latent factor method performs better than benchmark approaches. \\
\hline

\end{tabularx}
\normalsize
\end{table}

\section{Background}
Machine Learning with EDM has gained much more attention in the last few years. Many machine learning techniques, such as collaborative filtering \cite{toscher2010collaborative}, matrix factorization \cite{thai2011multi}, and artificial neural networks \cite{wang2011data} are being used to predict students' GPA or grades. In this section, we will describe these machine learning techniques and how they are being used to predict students' GPA in registered courses within the context of education.

\subsection{Collaborative Filtering}

Collaborative filtering (CF) is one of the most popular recommender system technique to date. In the educational context, the CF algorithms make predictions of GPA by identifying similar students in the dataset. In this method, predictions are made by selecting and aggregating the grades of other students. In particular, there is a list of $m$ students $S = \{s_1, s_2,..., s_m\}$ and a list of $n$ courses $C = \{c_1,c_2,...,c_n\}$. Each student $s_i$ has a list of courses $C_{si}$, which represents student GPA in a course. The task of CF algorithm is to find a student whose GPAs are similar to some other student. User-based Collaborative Filtering (UBCF) is one of the types of collaborative filtering technique. To predict the student GPA in a course, the UBCF algorithm considers similar students that have similar GPA in same courses. The main steps are:

\begin{enumerate}
  \item The algorithm measures how similar each student in the database to the active student by calculating the similarity matrix.
  \item Identify the most similar students by using $k$ nearest neighbors.
  \item Predict the GPA of the course of the active user by aggregating the GPA of that course taken by the most similar students. The aggregation can be a simple mean or weighted average by taking similarity between students into account.
\end{enumerate}

The $k$ nearest neighbour technique is used to select the neighbourhood for the active user $N(a) \subset U$. The average rating of the neighbourhood users is calculated using the equation \ref{eq:aggregation1}, which becomes the predicted rating for the active use. The grade prediction becomes extremely challenging for the student with a few courses attended which is a well-known drawback of CF technique over the sparse dataset.

\begin{equation}
\hat{r}_{aj}=\frac{1}{|N(a)|} \sum_{i \in N(a)} r_{ij}
\label{eq:aggregation1}
\end{equation}

\subsection{Matrix Factorization}
Matrix factorization is a decomposition of a matrix into two or more matrices. Matrix factorization techniques are used to discover hidden latent factors and to predict missing values of the matrix. In our study, we formulated the problem of predicting student performance as a recommender system problem and used matrix factorization methods (SVD and NMF) which are the most effective approaches in recommender systems.

\subsubsection{Singular Value Decomposition}
Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes students-courses matrix $R$ into

\begin{equation}
    R = U \Sigma V^T,
\end{equation}

where;

\begin{itemize}
  \item $U$ is an $m \times r$ orthogonal matrix, where $m$ represents number of users and $r$ represents the rank of the matrix $R$,
  \item $\Sigma$ is an $r \times r$ diagonal matrix with singular values along the main diagonal entries and zero everywhere else,
  \item $V$ is an $r \times n$ orthogonal matrix where $n$ represents the number of courses.
\end{itemize}

\begin{figure}[h]
    \centering
    \includegraphics[width=0.9\textwidth]{svd.eps}
    \caption{Decomposition of Matrix $R$ by SVD}
    \label{fig:svd}
\end{figure}

The graphical representation of SVD is shown in Figure \ref{fig:svd}. In newly constructed matrices, $r$ represents the rank of the matrix $R$. The values in the matrix $\Sigma$ are known as singular values $\sigma{_i}$, and they are stored in decreasing order of their magnitude. Each singular value $\sigma{_i}$ of the matrix $\Sigma$ represents hidden latent features, and their weights have variance on the values of matrix $R$. The sum of all elements represents the total variance of matrix $R$.

SVD is widely being used to find the best $k$-rank approximation for the matrix $R$. The rank $r$ can be reduced to $k$, where $k < r$, by taking only the largest singular value $k$ which is the first diagonal value of the matrix $\Sigma$ and then reduce both $U$ and $V$ accordingly. The obtained result is a $k$-rank approximation $R_k = U_k \Sigma_k V_k ^T$  of the matrix $R$, in such a way that the Frobenius norm of $R - R_k$ is minimized. The Frobenius norm $(|| R - R_k ||F)$ is defined as simply the sum of squares of elements in $R - R_k$ \cite{deerwester1990indexing}. To make a prediction of the GPA in a course, SVD assumes that each student grade is composed of the sum of preferences of the various latent factors of the courses. To predict the grade of a student $i$ for course $j$ is as simple as taking the dot product of vector $i$ in the student feature matrix and the vector $j$ in the course feature matrix.

The problem with SVD is that it is not effective on big and sparse datasets. Simon Funk proposed to use a Stochastic Gradient Descent (SGD) algorithm to compute the best rank-$k$ matrix approximation using only the known ratings of original matrix \cite{netflix2006}. Stochastic Gradient Descent (SGD) is a convex optimization technique that gets the most accurate values of those two featured matrices that are obtained during the decomposition of the original matrix in the method of SVD. SGD has following steps:

\begin{enumerate}
  \item Re-construct the target students-courses matrix by multiplying the two lower-ranked matrices.
  \item Get the difference between the target matrix and the generated matrix.
  \item Adjust the values of the two lower-ranked matrices by distributing the difference to each matrix according to their contribution to the product target matrix.
\end{enumerate}

Above is a repeated process till the difference is lower than a preset threshold.  By reducing the dimensionality of the students-courses matrix, the execution speed is reduced, and the accuracy of the prediction is increased because of considering only the courses that contribute to the reduced data. Dimensionality reduction leads to the reduction of noise and over-fitting. This method is also used in recommender systems for the Netflix challenge \cite{koren2009matrix}.

\subsubsection{Non-Negative Matrix Factorization}
Non-negative matrix factorization (NMF) is a matrix factorization technique that decomposes a matrix $V$ into two non-negative factor matrices $W$ and $H$ such that

\begin{equation}
    V \approx W H,
\end{equation}

where;

\begin{itemize}
  \item W is a $u \times k$ orthogonal matrix,
  \item H is a $k \times v$ orthogonal matrix.
\end{itemize}

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.6\textwidth]{nmf.eps}
    \caption{Decomposition of Matrix $V$ by NMF}
    \label{fig:nmf}
\end{figure}

Graphical representation of NMF is shown in Figure \ref{fig:nmf}. NMF is a powerful technique that uncovers the latent hidden features in a dataset and provides a non-negative representation of data \cite{koren2009matrix}. The problem with NMF is to find $W$ and $H$ when the dataset is large and sparse. A sequential coordinate-wise descent (SCD) algorithm can be used with NMF to impute the missing values \cite{franc2005sequential}. NMF imputation using SCD takes all entries into account when imputing a single missing entry.

\subsection{Restricted Boltzmann Machines}
The method of Restricted Boltzmann Machines (RBM) is an unsupervised machine learning method. Unsupervised algorithms are used to find the structural patterns within the dataset. We have used RBM to predict the students' performance in different courses. An RBM is in the form of a bipartite graph that creates two layers of nodes. The first layer is called the visible layer, which contains the input data (Course Grades). These nodes are connected to the second layer which is called the hidden layer that contains symmetrically weighted connections. From the Figure \ref{fig:rbm} we can see that the graph have five visible nodes (Course Grades) denoted by $v_i$ and four hidden nodes indicated by $h_j$. The weights between the two nodes are $w_{ij}$. Here each visible node $v_i$ represents the grade for course $i$, for a particular student.

\begin{figure}[h]
\centering
\begin{tikzpicture}[
plain/.style={
  draw=none,
  fill=none,
  },
net/.style={
  matrix of nodes,
  nodes={
    draw,
    circle,
    inner sep=11pt
    },
  nodes in empty cells,
  column sep=0pt,
  row sep=-1cm
  },
>=latex
]

\matrix[net] (mat)
{
|[plain]| \parbox{1.3cm}{\centering Hidden Nodes $h_j$}
&|[plain]| &|[plain]| & & |[plain]| & & |[plain]| & & |[plain]| &  \\
|[plain]| \parbox{1.3cm}{\centering Weights $w_{ij}$}
     &|[plain]| &|[plain]| &|[plain]| &|[plain]| &
    |[plain]| &|[plain]| &|[plain]| &|[plain]| &|[plain]| \\
|[plain]| \parbox{1.3cm}{\centering Visible Nodes $v_i$} &
    |[plain]| & & |[plain]| & & |[plain]| & & |[plain]| & & |[plain]| &\\
};
\foreach \ai [count=\mi ]in {3,5,7,9,11}
  \draw[<-] (mat-3-\ai) -- node[right] {Course \mi} +(0cm,-1.3);
\foreach \ai in {4,6,8,10}
{\foreach \aii in {3,5,7,9,11}
  \draw[-] (mat-1-\ai) -- (mat-3-\aii);
}
\end{tikzpicture}

\caption{\label{fig:rbm}
A Restricted Boltzmann Machines (RBM) with five courses and four hidden nodes for a specific student.
}
\end{figure}

RBM is a form of Markov Random Field (MRF). MRF is a type of probabilistic model that encodes the structure of the model as an undirected graph for which the energy function is linear in its free parameters. The energy function $E(v,h)$ for RBM can be calculated using the equation \ref{eq:energy}.

\begin{equation}
\label{eq:energy}
    E(v,h) = - a^Tv - b^Th - h^T Wv
\end{equation}

In the above equation, $W$ represents the weights between the hidden and visible nodes and $a$, $b$ are the offsets of the visible and hidden layers respectively. The probability distributions $P(v,h)$ of visible and/or hidden nodes can be calculated using the equation \ref{eq:distribution}.

\begin{equation}
\label{eq:distribution}
    P(v,h) =
        \frac{1}
        {Z}e^{-E(v,h)}
\end{equation}

Where $Z$ is a partition function that defines normalization of the distribution. To predict a student grade, one can include an additional visible node $v_p$, for which the value is unknown, but it can be determined by using the energy function given in the equation \ref{eq:prediction}.

\begin{equation}
\label{eq:prediction}
    P(v_p|v,h) \propto
        \frac{1}
        {Z}e^{-E(v_p,v,h)}
\end{equation}

\section{Methods}
We used CF (UBCF), MF (SVD and NMF) and RBM techniques to predict GPA of the student for the courses. A feedback model is developed based on the predicted GPA of the student in a course.

\subsection{Dataset Description}
A real world student data is collected from Electrical Engineering Department at ITU across students of the batch (2013, 2014, 2015). The dataset contains data of 225 undergraduate students enrolled in the Electrical Engineering program. The data of each student contains the students pre-university traits (secondary school percentage, high school percentage, entry test scores and interview), the course credits and the obtained grades of 24 different courses that the students take in different semesters. We consider only letter-grade courses but not fail courses. The information of courses and their domain is shown in Table \ref{table:course-domain}, which was obtained from the curriculum for Electrical Engineering designed for Pakistani Universities \cite{higher2012hec}.

\renewcommand{\arraystretch}{1.2}
\begin{table}[!h]
\caption{Courses Domain Table}
\label{table:course-domain}
\begin{tabular}{l p{3.7in}}
\hline
Course Domain & Courses \\
\hline
Humanities & Communication Skills I, Communication Skills II, Islamic Studies \\
Management Sciences & Industrial Chemistry, Entrepreneurship,  D Lab \\
Natural Sciences & Linear Algebra, Calculus and Analytical Geometry, Complex Variables and Transforms, Probability \& Statistics \\
Computing & Object Oriented Programming, Computing Fundamentals and Programming \\
Electrical Engineering Foundation & Linear Circuit Analysis, Electricity and Magnetism, Electronics Workbench, Electronic Devices and Circuits, Digital Logic Design, Electrical Network Analysis, Electronic Circuit and Design, Signals \& Systems \\
Electrical Engineering Core & Solid State Electronics, Microcontrollers and Interfacing, Electrical Machines, Power Electronics \\
\hline
\end{tabular}
\end{table}

\subsection{Problem Formulation}
For this study, we would like to predict student GPA from the scale 0.0 - 4.0. The given data we have is $\langle Student, Course, GPA \rangle$ triplet and we need to predict GPA for each student for the courses he/she will enroll in the future. In general, we have $n$ students and $m$ courses, comprising an $n \times m$ sparse GPA matrix $G$, where $\{G_{ij} \in R \mid G_{ij} \leq 4\}$  is the grade student $i$ earned in course $j$.

For training machine learning models, students grades need to be converted to GPA. These grades are converted to numerical GPA values using the ITU grading policy on a 4 point GPA scale with respect to the letter grades A+=4, A=4, A-=3.67, B+=3.33, B=3.0, B-=2.67, C+=2.33, C=2.0, C-=1.67, D+=1.33, D-=1.0 and F=0.0. Figure \ref{fig:grades-distribution} shows the frequency distribution of grades for the students whose grades are available in the dataset. We can see most of the students have B or B- grades in the courses they have taken.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.7\textwidth]{Grades-Distribution.eps}
    \caption{Distribution of students' grades received for the taken courses}
    \label{fig:grades-distribution}
\end{figure}

As prediction algorithm works best with centering predictor variables, so all the data were transformed by centering (average GPA of a course is subtracted from all GPAs of that course). The main characteristics of the dataset are shown in the Table \ref{table:exploratoryanalysis}.

\begin{table}[!h]
\centering
\caption{Description of ITU dataset used in this study}
\label{table:exploratoryanalysis}
\begin{tabularx}{\textwidth}{X r}
\hline
Characteristic & Number \\
\hline
Total students & 225 \\
Total courses & 24 \\
Total cells & 5400 \\
Elements (grades) available & 1736 \\
Elements (grades) missing & 3664 \\
Matrix density & 32.14\% \\
\hline
\end{tabularx}
\end{table}

\subsection{Prediction of Student Grades}
As our objective is to predict students GPA in the courses for which he/she needs to enroll in the future, we used CF (UBCF), MF (SVD and NMF) and RBM techniques to predict courses GPA of students. We take the data into a matrix in the form of $\langle Student, Course, GPA \rangle$ triplet. For illustration, here we have taken a few students and courses to display their grades. In the Table \ref{table:students-courses} we can see that a student with Id. SB145 have a GPA 3.67 in the course Electronic Circuit and Design and have a GPA of 4.0 in the D-Lab course. While this student needs to enroll into Linear Circuit Analysis, Islamic Studies, and Signals and System. A student with Id. SB185 have similar GPA in Electronic Circuit and Design course like the student with Id. SB145 and this student need to enroll into Linear Circuit Analysis, Islamic Studies, Signals and Systems, and D-Lab courses.

\begin{table}[h]
\centering
\caption{Students-Courses matrix with students' GPA in particular courses}
\label{table:students-courses}
\begin{tabularx}{\textwidth}{XXXXXX}
\hline
Student Id. & LCA & ECD & IS & SS & DL \\
\hline
SB145 & & 3.67 & & & 4 \\
SB161 & & 4 & & & 3.67 \\
SB185 & & 3.67 & & & \\
SB229 & & & & & \\
SB304 & 2 & & 2.67 & & \\
\hline
\end{tabularx}
\begin{tabular}{lll}
Linear Circuit Analysis (LCA) &  Electronic Circuit and Design (ECD) & Islamic Studies (IS) \\
Signals \& Systems (SS) & D-Lab (DL) & \\
\end{tabular}
\end{table}

\textbf{Collaborative Filtering:} We have used UBCF to predict the students' grades in courses. UBCF do grade prediction of a student $s$ in a course $c$ by identifying student grades in same courses as $s$. For prediction of grades, the neighborhood students $ns$ similar to student $s$ are selected that have taken at least $nc$ courses that were taken by student $s$. To apply UBCF model we first converted the students-courses matrix $R$ into a real-valued rating matrix having student GPA from 0 to 4. To measure the accuracy of this model we have split the data into 70\% trainset and 30\% testset. In UBCF model The similarity between students and courses is calculated using $k$ nearest neighbors.

\textbf{Matrix Factorization:} Matrix factorization is the decomposition of a matrix $V$ into the product of two matrices $W$ and $H$, i.e. $V \approx WH^T$ \cite{koren2009matrix}. In this study, we have used SVD and NMF matrix factorization techniques to predict the student GPA. The main issue of MF techniques is to find out the optimized value of matrix cells for $W$ and $H$.

In \textbf{SVD} approach, the students' dataset is converted into real-valued rating matrix having student grades from 0 to 4. The dataset is split into 70\% for training the model and 30\% for testing the model accuracy. We used Funk SVD to predict GPA in the courses for which the students are shown in Table \ref{table:students-courses} have not yet taken the courses. The largest ten singular values are 191.8012, 18.8545, 14.7946, 13.8048, 12.4328, 11.8258, 11.1058, 10.2583, 9.5020 and 9.1835. It can be observed from the Figure \ref{fig:svd} that the distribution of the singular values of students-courses matrix diminishes quite fast suggesting that the matrix can be approximated by a low-rank matrix with high accuracy. This encourages the adoption of low-rank matrix completion methods for solving our grade/GPA prediction problem.

\begin{figure}[!h]
    \centering
    \includegraphics[width=.75\linewidth]{svd-form.eps}
    \caption{Singular vales distribution of students-courses matrix}
    \label{fig:svd}
\end{figure}

By applying Funk's proposed heuristic search technique called Stochastic Gradient Descent (SGD) gradient to the matrix G we obtained two matrices student and courses dimensional spaces (with the number of hidden features set to two, so as to ease the task of visualizing the data). The stochastic gradient descent technique estimates the best approximation matrix of the problem using greedy improvement approach \cite{pelanek2015student}.

Table \ref{table:student-features} represents the students' features dimensional space, and Table \ref{table:course-features} represents courses' features dimensional space. With the dot product of these features dimensional space we can predict GPA in the courses for which the students are shown in Table \ref{table:students-courses} needs to enroll. Please note that we usually do not know the exact meaning of the values of these two-dimensional space, we are just interested in finding the correlation between the vectors in that dimensional space. For understanding, take an example of a movie recommender system. After matrix factorization, each user and each movie are represented by two-dimensional space. The values of the dimensional space represent the genre, amount of action involved, quality of performers or any other concept. Even if we do not know what these values represent, but we can find the correlation between users and movies using the values of dimensional space.

\renewcommand{\arraystretch}{1.5}
\begin{table}[h!]
\centering
\setlength\tabcolsep{4pt}
\begin{minipage}{0.48\textwidth}
\centering
\caption{Students' features dimensional space}
\label{table:student-features}
\begin{tabularx}{\textwidth}{Xrr}
    \hline
    Name & V1 & V2 \\
    \hline
    SB145 & 0.39 & 0.18 \\
    SB161 & 0.45 & 0.20 \\
    SB185 & 0.42 & 0.20 \\
    SB229 & -0.31 & 0.02 \\
    SB304 & 0.09 & 0.12 \\
    \hline
    \end{tabularx}
\end{minipage}%
\hfill
\begin{minipage}{0.48\textwidth}
\centering
\caption{Courses' features dimensional space}
\label{table:course-features}
    \begin{tabularx}{\textwidth}{Xrr}
        \hline
        Name & V1 & V2 \\
        \hline
        Linear Circuit Analysis & 1.19 & -0.04 \\
        Electronic Circuit and Design & 0.94 & 0.10 \\
        Islamic Studies & 1.77 & -0.03 \\
        Signals and Systems & 0.34 & 0.20 \\
        D-Lab & 0.46 & 0.18 \\
        \hline
    \end{tabularx}
\end{minipage}
\end{table}

In \textbf{NMF} approach, we have a $u \times v$ matrix $V$ with non-negative entries of student grades from 0 - 4 that decomposes into two non-negative, rank-$k$ matrices $W (u \times k)$ and $H (k \times v)$ such that $V \approx WH$. Before decomposing a matrix into two matrices first, we need to choose a rank-$k$ for NMF that gives the smallest error for grade predictions of the students-courses matrix. In our experiments with NMF, the rank-$k$ 2 gives the minimum Mean Squared Error (MSE) as shown in the Figure \ref{fig:nmf-rank}. So, we have used two as rank-$k$ value and decomposed the matrix into $W$ and $H$.

\begin{figure}[!h]
    \centering
    \includegraphics[width=.75\linewidth]{Rank.eps}
    \caption{Rank-$k$ using NMF}
    \label{fig:nmf-rank}
\end{figure}

\textbf{Restricted Boltzmann Machines:}
We have also used RBM an unsupervised learning technique to predict the student grades in different courses. RBM has been used to fill the missing data in a students-courses matrix. We have split the data into 70\% trainset and 30\% testset. We have trained the RBM method with a learning rate of 0.1, momentum constant of 0.9, the batch size of 180, and for 1000 epochs.

\subsection{Feedback Methodology}
Machine learning techniques can be utilized to identify the weak students who need appropriate counseling/advising in the courses, by early predicting the courses grades. A feedback model
that we have developed will calculate the student's knowledge in the particular course domain based on the results it gives feedback to the instructor about the courses in which a student is weak. The detail of the feedback model is given below and represented in a Figure \ref{fig:flow-diagram}.

\begin{figure}[h]
    \centering
    \includegraphics[width=0.99\textwidth]{feedback.eps}
    \caption{Main steps of feedback model}
    \label{fig:flow-diagram}
\end{figure}

\begin{enumerate}
  \item \textbf{Build Student Profile:} In the first phase of feedback model; we have to parse students and courses data into the form of $\langle Student, Course, GPA \rangle$ triplet to built students' profile. A students-courses matrix $R$ is created that contains students' performance in each course taken. In a matrix $R$, students are represented in rows and courses are represented in columns. The value of each cell of matrix $R$ is $R_{ij}$, that can be calculated using the equation \ref{matrix-entry-equation}.

\begin{equation}\
\label{matrix-entry-equation}
R_{ij} =   \left \{
  \begin{tabular}{lr}
  student's $i$ mark on course $j$, & if the student enrolled in course $j$ \\
  empty, & if the student did not enroll in course $j$ \\
  \end{tabular}
\right \}
\end{equation}

For the courses in which a student did not enroll, $R_{ij}$ will be empty. For illustration, a small chunk of the dataset is presented in matrix given below. This matrix holds the dataset of five different students and five different courses.

\begin{equation}\
\label{students-courses-matrix}
R_{ij} = \left \{
  \begin{tabular}{llllll}
   & & 3.67 & & & 4 \\
   & & 4 & & & 3.67 \\
   & & 3.67 & & & \\
   & & & & & \\
   & 2 & & 2.67 & & \\
   \end{tabular}
\right \}
\end{equation}

\item \textbf{Predict Course GPA:}
Now we have a matrix $R$, for which we are interested to find the unknown GPAs for the courses, which the student has not taken yet. To find the predicted GPA we have used CF (UBCF), MF (SVD and NMF), and RBM techniques. Detailed methodology for these techniques is described in section 4.

\item \textbf{Students' Knowledge in Course Domain:}
In our feedback model, student knowledge in different course domains is calculated by taking an average of GPAs for the courses the student has taken which fall into the same domain by using the course domain table (Table \ref{table:course-domain}).

\item \textbf{Knowledge Inference:}
Hidden Markov Model (HMM) is a model used to predict students' performance based on their historical performance. According to the model, the probability of knowledge $P(L_j)$ increases with every step $j$ and can be calculated using the equation \ref{bkt-equation}.

\begin{equation}\
\label{bkt-equation}
P(L_j) = P(L_{j-1}) + P(T) (1 - P(L_{j-1})),
\end{equation}

where;

\begin{itemize}
  \item $P(L_j)$ is the probability of knowledge in the step $j$,
  \item $P(L_{j-1})$ is the probability of knowledge in the previous step,
  \item $P(T)$ is the probability of learning,
  \item $(1 - P(L_{j-1})$ is the knowledge that is unknown.
\end{itemize}

Using the equation \ref{bkt-equation}, student knowledge is measured by inferring his knowledge in the course domain. As we know the probability of the knowledge in the previous step is the predicted GPA for the student in the subject. To calculate the knowledge gain course domain average has been converted into the range (0 to 1) and multiplied by the learning rate 0.005.

\item \textbf{Feedback:}
After computing the student knowledge in particular course domain and knowledge inference, the feedback is made. If the student knowledge inference results are less than 2.67 GPA in a course, then the system generates a warning that the student needs effort in that course. In this way, feedback results can inform the instructors that the student is weak in a particular course.
\end{enumerate}

\section{Results}

\subsection{Correlation Analysis}
To find the pre-admission factors (SSC, HSSC, entry test and interview) that can predict student performance in the university Pearson Correlation has been applied. The result shows that there is a positive correlation between entry test and Cumulative Grade Point Average (CGPA) and also between HSSC and CGPA. The correlation coefficients ($r$) between the entry test and CGPA, and HSSC and CGPA are very close ($r$ = 0.29 and $r$ = 0.28 respectively), indicating that both entry test and HSSC are equally important in predicting the CGPA of a student. Figure \ref{fig:linear-regression-fitting-2} shows the correlation between the entry test of the students and their CGPA, and Figure \ref{fig:linear-regression-fitting} shows the correlation between the higher secondary school performance and the CGPA. These figures show that the students with a higher score in entry test and a higher percentage in HSSC performance obtain higher CGPA in the degree program.

\begin{figure*}[!h]
\centering
\begin{minipage}[b]{.45\textwidth}
\includegraphics[width=.99\linewidth]{Linear-Regression-Fitting-2.eps}
\caption{Correlation between entry test and CGPA}
\label{fig:linear-regression-fitting-2}
\end{minipage}\qquad
\begin{minipage}[b]{.45\textwidth}
\includegraphics[width=.99\linewidth]{Linear-Regression-Fitting.eps}
\caption{Correlation between HSSC and CGPA}
\label{fig:linear-regression-fitting}
\end{minipage}
\end{figure*}

\subsection{Grade Prediction}
For students, GPA prediction, students-courses matrix G is constructed. The data were transformed by centering the predictor variables by taking average GPA of a course and subtracted it from all GPAs of that course. 70\% of the dataset is used for training the CF MF and RBM models. Student GPAs for the courses has been predicted and displayed in Table \ref{table:gpa-predictions}.

\renewcommand{\arraystretch}{1.1}
\begin{table}[!h]
\caption{Student GPA prediction in courses based on CF, SVD, NMF and RBM technique}
\label{table:gpa-predictions}
\begin{tabularx}{\textwidth}{XXXXXXX}
\hline
Student Id. & Method & LCA & ECD & IS & SS & DL \\
\hline
SB145 & RBM & \textbf{2.67} & 3.67 & \textbf{2.33} & \textbf{3} & 4 \\
 & NMF & \textbf{1.86} & 3.67 & \textbf{1.99} & \textbf{3.61} & 4 \\
 & SVD & \textbf{3.48} & 3.67 & \textbf{3.86} & \textbf{3.1} & 4 \\
 & UBCF & \textbf{2.99} & 3.67 & \textbf{2.99} & \textbf{2.91} & 4 \\
\hline
SB161 & RBM & \textbf{2.67} & 4 & \textbf{3} & \textbf{2.67} & 3.67 \\
 & NMF & \textbf{2.77} & 4 & \textbf{2.88} & \textbf{3.44} & 3.67 \\
 & SVD & \textbf{2.99} & 4 & \textbf{3.86} & \textbf{2.63} & 3.67 \\
 & UBCF & \textbf{2.41} & 4 & \textbf{2.81} & \textbf{2.39} & 3.67 \\
\hline
SB185 & RBM & \textbf{2.67} & 3.67 & \textbf{3} & \textbf{3.33} & \textbf{3} \\
 & NMF & \textbf{2.53} & 3.67 & \textbf{2.64} & \textbf{3.42} & \textbf{3.60} \\
 & SVD & \textbf{2.31} & 3.67 & \textbf{3.36} & \textbf{3.35} & \textbf{2.12} \\
 & UBCF & \textbf{1.84} & 3.67 & \textbf{2.51} & \textbf{2.93} & \textbf{2.12} \\
\hline
SB229 & RBM & \textbf{2.33} & \textbf{2} & \textbf{3.33} & \textbf{2} & \textbf{1.33} \\
 & NMF & \textbf{2.03} & \textbf{0.98} & \textbf{2.04} & \textbf{0.63} & \textbf{1.03} \\
 & SVD & \textbf{2.09} & \textbf{1.79} & \textbf{2.12} & \textbf{1.27} & \textbf{2.25} \\
 & UBCF & \textbf{2.77} & \textbf{2.19} & \textbf{3.14} & \textbf{1.42} & \textbf{2.43} \\
\hline
SB304 & RBM & 2 & \textbf{3} & 2.67 & \textbf{2} & \textbf{3} \\
 & NMF & 2 & \textbf{3.32} & 2.67 & \textbf{3.33} & \textbf{3.43} \\
 & SVD & 2 & \textbf{2.36} & 2.67 & \textbf{1.61} & \textbf{2.57} \\
 & UBCF & 2 & \textbf{2.19} & 2.67 & \textbf{1.42} & \textbf{2.43} \\
\hline
\end{tabularx}
\begin{tabular}{lll}
Predicted GPAs are in \textbf{bold} & Linear Circuit Analysis (LCA) &  Electronic Circuit \& Design (ECD) \\ Islamic Studies (IS) & Signals \& Systems (SS) & D-Lab (DL) \\
\end{tabular}
\end{table}

\subsection{Evaluation on model performance}
There are several types of measures for evaluating the success of models. However, the evaluation of each model depends heavily on the domain and system's goals. For our system, our goal is to predict students' GPA and make decisions if a student needs to work hard to complete the course. These decisions work well when our predictions are accurate. To achieve it, we have to compare the prediction GPA against the actual GPA for the students-courses pair. Some of the most used metrics for evaluation of the models are the Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE). We evaluated model predictions by repeated random subsample cross-validation. We performed ten repetitions. In each run, we choose randomly 70\% of students data into the train set and 30\% of students data into the test set. We have computed RMSE, MSE, and MAE for each model. From Figure \ref{fig: error} the results show that the RBM model provides a clear improvement over the CF and MF models. Please note we are not performing student-level cross-validation of predicted results on newly registered students in this study but the currently enrolled students.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.73\textwidth]{errors.eps}
    \caption{Evaluation of grade prediction models}
    \label{fig: error}
\end{figure}

\subsection{Feedback Model}

The results of feedback model that was discussed in detail in section 4 are shown in Table \ref{table:recommendation-results}. Here we put one of the students (SB185) to demonstrate the results of feedback model. We can see that the knowledge inference results of a student in Linear Circuit Analysis are less than 2.67, so the system gives a warning that the effort is needed in this course. These results are helpful for an instructor to identify weak students in a course by early predicting the grades and inferring student knowledge in the course domain.

\begin{table}[!h]
\centering
\caption{Feedback Model Result of Student (SB185)}
\label{table:recommendation-results}
\begin{tabular}{p{0.5in}p{0.6in}p{0.6in}p{1.5in}p{0.6in}p{0.7in}p{0.5in}}
  \hline
Course & Predicted Grade & Predicted GPA & Course Domain & Domain Average & Knowledge Inference & Effort Needed \\
  \hline
LCA & B & 3 & Electrical Engineering Foundation & 3.07 & 2.12 & YES \\
IS & B & 3 & Humanities & 3.19 & 3.83 & \\
SS & B- & 3.07 & Electrical Engineering Foundation & 3.4 & 2.91 & \\
DL & B+ & 3.33 & Management Sciences & 3.12 & 3.44 & \\
  \hline
\end{tabular}
\begin{tabular}{lll}
Linear Circuit Analysis (LCA) &  Electronic Circuit and Design (ECD) & Islamic Studies (IS) \\
Signals \& Systems (SS) & D-Lab (DL) & \\
\end{tabular}
\end{table}

\section{Insights} In this study, we have used CF (UBCF), MF (SVD and NMF) and RBM techniques to predict the students' performance in the courses. CF is a popular method to predict the students' performance due to its simplicity. In this technique, the students' performance is analyzed by using the previous data. It provides feedback to enhance the students' learning process based on the outcome of the analysis. However, this method has several disadvantages: since it depends upon the historical data of users or items for predicting the results. It shows poor performance when there is too much sparsity in the data, due to which we are not able to predict the students' performance accurately. Comparatively, in SVD technique, the data matrix $R$ is decomposed into users-features space and items-features space. When SVD technique is used with gradient descent algorithm to compute the best rank-k matrix approximation using only the known ratings of $R$, the accuracy of predicting the students' performance enhances but it may contain negative values which are hard to interpret. NMF technique enhances the meaningful interpretations of the possible hidden features that are obtained during matrix factorization. RBM is an unsupervised machine learning technique that is suitable for modeling tabular data. It provides efficient learning and inference better prediction accuracy than matrix factorization techniques. The use of RBM in recommender systems and e-commerce have also shown good results \cite{kanagal2012supercharging}. From the above discussion, it is clear that the RBM technique outperforms CF and MF techniques with lesser chances of error. The overall result obtained in this study also shows that RBM surpasses other techniques in predicting the student's performance.

\section{Limitations}
We note that the reported findings of this study have been based on the dataset of the performance of the undergraduate students from ITU. The dataset used in the study is limited with GPAs available for students in the particular courses. After using CF (UBCF), MF (SVD and NMF) and RBM techniques on the dataset, we can see that the RMSE for RBM technique is lower compared to the RMSE of other techniques. RMSE can be estimated with more clear results if more information of the students' GPAs is available. Student motivation during studies also plays a significant role in the prediction of student success which can be considered in future study related to the grade prediction. Moreover, there is a need to improve the prediction results by dealing with the cold-start problems. Also, models based on tensor factorization can be investigated to take the temporal effect into account in the student performance prediction. Despite these limitations, our research findings have important practical implications for the universities and institutes in enhancing their students' retention rate.

\section{Conclusion}
Early GPA predictions are a valuable source for determining student's performance in the university. In this study, we discussed CF (UBCF), MF and RBM techniques for predicting student's GPA. We use RBM machine learning technique for predicting student's performance in the courses. Empirical validation on real-world dataset shows the effectiveness of the used RBM technique. In a feedback model approach, we measure the students' knowledge in a particular course domain, which provides appropriate counseling to them about different courses in a particular domain by estimating the performance of other students in that course. This feedback model can be used as a component of an early warning system that will lead to students' motivation and provides them early warnings if they need to improve their knowledge in the courses. It also helps the course instructor to determine weak students in the class and to provide necessary interventions to improve their performance. In this way rate of the students' retention can be increased.

\bibliographystyle{acmtrans}

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Nick Gibb MP, Minister for Schools Standards, described education as "the foundation of our culture".
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text:
Q: Why can I use the global function fetch after the src is bundled with webpack? Although I haven't included the fetch module in webpack? I am confused about the usage of fetchin webpack.
As long as I pack the fetch with webpack, an error occurs, but when I remove the fetch module from webpack, it works as well.

A: The reason you are able to use fetch even though you didn't include it in your bundle is because fetch is an official API that is supported by all modern browsers:

console.log(
  typeof window.fetch // function on modern browsers
);

The main reason people install a npm package with the same API is to handle environments without a built-in fetch API, such as older browsers.

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Tim Raines, Jeff Bagwell, Ivan Rodriguez Elected to Baseball Hall of Fame
The Baseball Hall of Fame will admit three new members in July: Tim Raines, Jeff Bagwell and Ivan "Pudge" Rodriguez. This year was Raines' final year of eligibility, while Rodriguez cleared the 75 percent barrier on his first try. This was Bagwell's seventh time on the ballot.
Donald Trump Wins Election, Defeats Hillary Clinton [UPDATED 2:45 a.m. ET]
It's finally over, and it might be the biggest upset in American political history.
Trump vs. Clinton, Round 2 — A Preview of Sunday Night's Second Presidential Debate
On Sunday night, presidential candidates Donald Trump and Hillary Clinton will meet in a town-hall setting at Washington University in St. Louis. The event starts at 9 p.m. ET and will run for 90 minutes.
New York Bombing Suspect Arrested After Shootout With Police in N.J. (UPDATED)
Law enforcement has taken into custody Ahmad Khan Rahami, the man wanted in connection to the weekend bombings in New York City and Seaside Park, New Jersey.
U.S. Olympic Swimmers' Robbery Story Was Fabricated; Surveillance Video Emerges From Gas S…
American Olympians Ryan Lochte, Jack Conger, Gunnar Bentz and Jimmy Feigen, who had told the media that they had been held up at gunpoint the night of August 13 in Rio de Janeiro, may have fabricated their story, possibly in an attempt to cover up their behavior at a gas station.
Truck Driver in Bastille Day Terror Attack Identified; Death Toll Rises to 84, Includes 2 …
Some updates on Thursday's horrific events in Nice, France.
Scores Dead in France as Truck Runs Over Crowds of People During Celebration
A man drove a truck through a huge crowd of people at a beach in Nice, France, killing at least 73 and injuring many more. The driver has been killed by police.
Donald Trump to Name Mike Pence as Running Mate
Sources report that Donald Trump will announce his selection of Indiana Governor Mike Pence as his running mate.
Woman Live-Streams Aftermath of Police Fatally Shooting Her Boyfriend During Traffic Stop
A 32-year-old man is dead after being shot by a police officer in his car.
FBI Recommends No Charges Against Hillary Clinton in Email Probe
FBI Director James Comey announced Monday morning that the agency will not recommend prosecutorial charges against former Secretary of State Hillary Clinton. The FBI had been investigating her usage of a private email server during her tenure at the State Department.
Adult Weekend Pass to Bell County Comic Con


London Zoo's immersive Land of the Lions exhibit transports visitors to an Indian village (whilst in the heart of London).
The exhibit brings educational and conservation messages about Asiatic lions through theming and props.
The posters, whilst part of the theming, also provided interesting information about the animals. The poster above even had a hole for the mongooses to tunnel through into their outside enclosure!