Data-driven educational algorithms pedagogical framing

Data from students and learning practices are essential for feeding the artificial intelligence systems used in education. Recurrent data trains the algorithms so that they can be adapted to new situations, either to optimize coursework or to manage repetitive tasks. As the algorithms spread in different learning contexts and the actions which they perform expand, pedagogical interpretative frameworks are required to use them properly. Based on case analyses and a literature review, the paper analyses the limits of learning practices based on the massive use of data from a pedagogical approach. The focus is on data capture, biases associated with datasets, and human intervention both in the training of artificial intelligence algorithms and in the design of machine learning pipelines. In order to facilitate the adequate use of data-driven learning practices, it is proposed to frame appropriate heuristics to determine the pedagogical suitability of artificial intelligence systems and also their evaluation both in terms of accountability and of the quality of the teaching-learning process. Thus, finally, a set of topdown proposed rules that can contribute to fill the identified gaps to improve the educational use of data-driven educational algorithms is discussed.

datos generados recurrentemente son fundamentales para entrenar los algoritmos, de manera que puedan adaptarse a nuevas situaciones, ya sea para mejorar el ciclo de aprendizaje en su conjunto o para gestionar tareas repetitivas. A medida que los algoritmos se propagan en diferentes contextos de aprendizaje y se amplía su capacidad de acción, se requieren marcos pedagógicos que ayuden a interpretarlos y que amparen su uso adecuado. Basándose en el análisis de casos y en una revisión de la literatura científica, en este artículo se analizan los límites de las prácticas de aprendizaje fundamentadas en el uso masivo de datos desde un enfoque pedagógico. Se toman en consideración procesos clave como la captura de los datos, los sesgos en las bases de datos y el factor humano que está presente en el diseño de algoritmos de inteligencia artificial y de sistemas de Aprendizaje Automático. Con el fin de facilitar la gestión adecuada de los algoritmos educativos basados en datos, se plantea la idoneidad de introducir un marco pedagógico que permita analizar la adecuación de los sistemas de inteligencia artificial y apoyar su evaluación, considerando su impacto en el proceso de aprendizaje. En ese sentido, se propone finalmente un conjunto de reglas de enfoque heurístico con el fin de mejorar los vacíos pedagógicos identificados y que puedan apoyar el uso educativo de los algoritmos basados en datos.
The ability to access directly the large amounts of data from online learning platforms is affecting the establishment of the purposes, procedures and the very consideration of educational practices based on digital data. At the same time, the growth of digital learning spaces is boosting basic research on learning processes based on the huge volume of digital data available.
In order to address the challenges of massive data analysis in the study of digital learning experiences, new disciplines -such as learning analytics (Siemens et al., 2011;Buckingham & Ferguson, 2012;Greller & Drachsler, 2012)-combining computer science, mathematics and applied statistics have been introduced (Gitelman, 2013;Kitchin, 2014). Educational research is also increasingly using automatic processes that rely on available information to intervene directly in the learning cycle -i.e. predictive learning analytics, student modelling, recommendation systems, or educational process trace analysis (Breslow et al., 2013;Thille et al., 2014) are all methods that use Artificial Intelligence (AI) algorithms to adapt course design to student needs-. In addition, digital data are also used to design and train Machine Learning (ML) based applications to guide students, and monitor and evaluate learning (Hew, Qiao, & Tang, 2018;Hussain, Zhu, Zhang, Abidi, & Ali, 2019).
Along with the emergence of new methods and disciplines, there is a debate about the change involved in accessing information on student behaviour directly, without previous filters or, at least, without the type of conceptual and methodological filters used previously -i.e. statistical inference, sampling, theoretical framing, etc.-. And, in the same way, radical changes are being discussed in the epistemic conditions that support the ethical regulation of research and intervention in students' daily lives (Crawford, 2016;Farrow, 2016;Metcalf, Keller, & boyd, 2016;Amo et al., 2019).
In that context, this document attempts to frame the main current debates on the use of AI in education by providing a pedagogical view from the educational sciences (Goksel & Bozkurt, 2019;Luckin & Cukurova, 2019;Sharma, Kawachi, & Bozkurt, 2019;Sloane & Moss, 2019;UNESCO, 2019;Zawacki-Richter, Marín, Bond, & Gouverneur, 2019). AI is the combination of a certain type of technologyan algorithm-and a large set of data; and it also includes non-human data, product design and the software used (Sinders, 2019a). AI-based systems and products can affect learning in many ways and, above all, is currently changing the face of educational research and technological interventions aimed at improving the learning cycle. Thus, applying AI in learning contexts involves addressing many of the conceptual and epistemic concerns of data-based educational research (Domínguez, Álvarez, & Gil-Jaurena, 2016).
The paper discusses the pedagogical principles associated with data-driven educational algorithms in order to provide useful rules to guide their design and application in educational spaces. According to the previous analysis by Houlden & Veletsianos (2019), a critical and relevant example-based approach together with a literature review is applied here to conduct the analysis. Firstly, the importance of the human component in the design of AI and ML systems is described. It then analyses the need to introduce a pedagogical dimension that frames the specifically educational aspects arising from data privacy, algorithmic biases and enhanced surveillance systems. Finally, based on the identified pedagogical elements, a heuristic approach is used to propose a set of rules to guide the design and evaluation of data-driven AI applications in education. It is intended to serve as a theoretical precedent to empirically validate a set of criteria for the implementation of AI-based learning systems in education.

THE HUMAN FACTOR IN EDUCATIONAL ALGORITHMS
The data determine much of what educational algorithms do. The data that feed the educational algorithms are a variety of inputs that people make, such as what they choose to like online, what they comment on, how often they check something, and when they use something. They are constantly feeding into the algorithm within the myriad of existing AI-based products, such as recommendation systems, text editors, conversation robots, or activity supervisors. In this way, the data are activated: they have a particular purpose and can become as important as the code of the algorithm (Sinders, 2019b).
But the data is not the main element that determines how the algorithms behave. System design and, especially, human decisions about how to combine data sets are fundamental to understanding how an algorithm uses data.

Core decisions in predictive analytics
This is the case, for example, with learning recommendation systems, which is one of the outstanding features in e-learning products and also supports institutional strategies for student recruitment and retention (Bodily & Verbert, 2017;Prabhakar, Spanakis, & Zaiane, 2017;Romero & Ventura, 2017). In general, recommendation systems are algorithms that aim to suggest relevant elements to users such as movies to watch, products to buy, text to read, learning activities to do, or courses to enrol in. In education, recommendation systems are the main product of predictive analysis, which many colleges and universities use to achieve their student recruitment objectives, focusing on enrolment strategies and adjusting scholarship policies. Demographic and performance data can help educational institutions predict whether a student will enrol in a course, whether once enrolled he/she will stay on track during his/her learning cycle, and whether he/she will require support not to fall behind before completion. Predictive analytics are also used to better tailor counselling services and to personalize learning with the goal of improving student performance (Domínguez, 2018).
To explain how these systems work, as well as the human component in algorithm modelling, the case of Spotify's recommendation app called Discover Weekly is described (see Figure 1). Discover Weekly is a playlist of songs created from a combination of user data and algorithmic inference. In order to display a suitable playlist to a target person, the system initially relies on other people's playlists. Spotify commences by looking at all the playlists created by users, which contain a reflection of their interests and sensitivities. These human-made song selections and groupings are at the heart of Discover Weekly's recommendations. From there, the algorithm gives extra weight to the company's own playlists and the lists that have the most followers. It then attempts to fill in the gaps between the target person's listening habits and those with similar interests. Consequently, if Spotify detects that two of the target user's favourite tracks tend to appear in other playlists along with a third track that the target has not listened to before, it will suggest the new track. In addition, Spotify also creates a profile of each user with their particular music interests, grouped into singer sets and music genres. Finally, the algorithms are responsible for connecting the data from the millions of playlists and the personal interest profile (Pasick, 2015;Sinders, 2019c).
The approaches behind this process of configuring Spotify's algorithms include collaborative filtering and natural language processing, which are automatic selection systems, along with deep learning, which is a technique for recognising patterns in huge amounts of data using powerful computers that are trained by humans to improve their selections (Johnson & Newett, 2015).  In educational contexts, automatic referral systems meet the same requirements as Discover Weekly. To make the results fit the interests of the students, it requires previous access to the trace data generated in the interaction with the educational software, mainly with the Learning Management Systems (LMS). The decisions about which data to obtain or how to combine them do not correspond to the algorithm, but to the people in charge of modelling the information and designing the automatic processes that will later be executed by the algorithm. When it happens in learning contexts, many questions arise that have a clear pedagogical component.
On the one hand, students may wonder how a certain sequence of recommendations came to exist. Which concrete data trained the algorithm. Whether the algorithm infers only from the learning habits of a single student, or whether it takes into account the most popular patterns among the set of actions performed by all students in the LMS. If it takes into account one gender over another, or the time when the actions happen. Whether the actions made by friends -i.e. people you have contact with within the LMS, or eventually outside on social networks-have an effect on the suggestions made.
What is more, from the perspective of the teacher who uses AI-based software in the classroom (Smith, 2019), it is necessary to know the rudiments behind the technologies employed. To improve teaching, it is equally necessary to have the ability to adapt the system to the specific learning practices that arise spontaneously. This aims to prevent the biases and issues associated with current AI-based learning systems which, as mentioned above, require human intervention -and in this case, also the application of a pedagogical vision-decisively to operate properly in a given learning context.

Machine learning pipelines in educational contexts
In addition to AI systems for recommendations, there are educational applications of ML -a subset of AI-especially oriented to the grading process (Alsuwaiket, Blasi, & Al-Msie'deen, 2019), predictive analytics (Uskov, Bakken, Byerly, & Shah, 2019), and identification of learning paths adapted to each student (Kurilovas, 2019). And as it happens with the data-based AI applications, also in the design of educational systems based on ML there is an outstanding human component that requires a pedagogical approach.
ML pipelines consists of the steps to train a data model. It helps to automate the workflows leading to the design of an ML algorithm. It is a cyclical and iterative process, as each step is repeated to continuously improve the accuracy of the model and to have an efficient algorithm. Many of the current ML models are trained neural networks, capable of executing a specific task or providing knowledge derived from what happened to what is likely to happen. They are complex models that are never completed. Rather, through repetition of mathematical or computational procedures, they are applied to the previous result and improved each time to obtain closer approximations to problem solving. Thus, a huge amount of data, processed iteratively, are required to provide the resources to train the ML models (see Figure  2).
One last element to consider in order to obtain good results in the processing of large volumes of data, is the value of the metadata. Metadata resides with the captured data and provides descriptive information about the digital objects -which aggregate data-and the autonomous data. Metadata extraction and correlations between them are the basis of ML models. This is due to the need to work with tags in order to associate data that considered independently would be difficult to handle with each other (Zhou, 2018). Moving that process into the field of education, the main consideration relates to the types of essentially educational tasks required to work with ML systems. Designing the model, training the model, and testing and tagging the data are all human tasks. People are needed to train the models, because currently this task cannot be done without the participation of people. And it is those people who make decisions about what happens to the ML systems, where they are going to be used and for what purposes.
The main pedagogical concerns here are related to the evaluation of the whole system, so that the training of the model is properly oriented to the requirements in terms of learning improvements, without deviations, once several iteration cycles have passed. Additionally, we must also consider the adequate pedagogical approach of the whole system, in terms of fostering the adequate development of skills and competencies of students (Reich, 2014).

MISALIGNMENTS IN DESIGNING DATA-DRIVEN ALGORITHMS
Over the past few years educational sciences have developed a set of conceptual, policy and institutional resources based on how to work with data from learning practices. But AI educational systems are questioning the strict application of that framework to the case of digital data. When researching in a digital context, many open questions arise on substantive issues: whether research methods and programmes based on digital data should be excluded from current ethical frameworks, or are required to comply with existing standards; whether these current standards should be adapted to the special circumstances of digital systems, or whether completely new standards and institutional commitments are needed.
So working on AI requires expanding the framework for educational research. Data from students' digital practices become -at least, in theory-indefinitely connectable and reusable, continuously updateable and easily removable from the context in which they were collected (boyd & Crawford, 2012;Zwitter, 2014). These features that characterize digital systems challenge the limits corresponding to analogue practices, which depend on data that are bounded in time and context, and which are highly constrained by technical infrastructure and financial cost.
A set of methodological challenges associated with the educational use of automatic data processing technologies is analysed below. The concerns involved in the socio-educational use of data-based technologies are raised (Tufekci, 2013;Pitcan, 2016;Bulger, 2016;Caplan, Donovan, Hanson, & Matthews, 2018;Perrotta & Selwyn, 2019), and from there a renewed approach is provided to improve learning based on the management of students' digital data.

Data set and platform bias
As mentioned, to suggest recommendations predictive AI systems study people's behaviour and relate it to some pattern that can explain their actions and, especially, predict their behaviour in the future. In the case of e-learning, the data analysed come from highly complex situations, with multiple meanings and whose interpretation depends largely on the context in which they have been collected. The main element that determines the context is the specific digital platform where the learning activity takes place. This is so important that the same behaviour could have different meanings depending on the platform on which it occurred.
For example, in the case of research on social behaviour on the Internet, the most analysed platform has been Twitter. However, Twitter is far from being a platform that represents the set of digital applications that allow social interaction. Each platform incorporates certain specific functionalities that may not be representative of other social platforms or of human social behaviour in general.
As for Twitter and social networks, in education the platform that has been most researched from learning analytics methodologies has been Open edX. This is mainly due to the fact that it is a free, open source tool that was originally developed for the courses of the edX project, which is the main MOOC site on the Internet.
The multiple studies and experiments on student activity in Open edX have led academics to suggest a general framework for student behaviour in online courses. The framework addresses such important issues as communication in the forums, course completion rates and teacher assignments. However, the Open edX platform does not have some of the features that are common and widely used in other tools, such as Moodle, Canvas or Blackboard platforms, which are leaders in the LMS market. For example, Open edX differs from Moodle in aspects such as the integration of visual elements into text, the monitoring of forum discussions or the management of assessment tests. Open edX's simple interface is well suited for use on mobile devices, making it the preferred platform for studying in mobile situations or from low-bandwidth environments. The mechanism for consulting video classes also causes a particular behaviour, since it is based on a series of viewing rules that are not necessarily equivalent or correspond to the way audio-visual content is consumed on other digital platforms.
To compensate for the shortcomings of the single-platform research models, the data sets involved should be extended to cover the emerging ecology of the contexts that are related to the phenomenon under analysis (Ruipérez-Valiente, Halawa, Slama & Reich, 2019). This does not mean that nothing valuable can be investigated from a single-platform analysis. Rather, it is to assume that these analyses are examining a closed system. And that, ultimately, the solution to this limitation of research based on specific data sets may not be solved by learning analytics methodologies alone.

Searching for tags and keywords in single case studies
Many educational studies with big data -later taken as a reference for modelling AI software-extract relevant text from a platform using tags or keywords. For example, in a course's virtual forum, messages are analysed for words such as exam, query, or thanks. While studies based on tags and keywords can be a powerful method to examine the flow and subject matter of conversations in a course, they are analyses built on the basis of selecting the dependent variable, which is the one that corresponds to the case under study, with all the characteristics and weaknesses that entails using such a methodological route.
In a social investigation, a sample comprising one or several cases has limited analytical power and could offer misleading results, since the variation in the dependent variable is limited (Geddes, 1990). For example, if research is conducted on the essential conditions for students to better understand a topic within a course by looking only at cases of successful courses that have occurred, the explanatory power will necessarily be limited. To improve explanatory power, it would be necessary to also include cases that might have similar characteristics, but where failures have occurred and students have not adequately understood the topics. In the same vein, in keyword-based datasets, a message is included in the dataset precisely because it has a particular outcome already associated with it. In addition, most keywords used to create large datasets are examples of successful terms, which are well known, widely distributed and generate great interest. This calls into question the capacity of this type of study and points to the need to open up the design of research by incorporating a wider variety of techniques and instruments for analysis.

Correlation does not imply causation, even for algorithms
Related to the above assumption, there is a close relationship between the selection of dependent variable features and the attribution of specific factors on which the uncorrelated sample features depend. That is, a self-selected population will not only have general characteristics different from those of the general population, but may also exhibit significantly different correlation trends. This creates -at least-two types of problems.
On the one hand, there is confusion in the variables analysed. Following the example of the tags associated with a message in a forum, these are often related to assumptions, meanings and the cultural or political structure of the context where the conversation takes place. Therefore, the use of tags, in addition to being a method of self-selection, often involves participation and commitment to the framework that the tag integrates. The biases inherent in this situation prevent the conclusions from being generalized to other contexts, which limits the research.
However, the main mistake that research designs that confuse the dependent and independent variable can make is the assumption that the correlation between the factors or traits observed simultaneously in the variables implies some kind of causality between them. This is a common fallacy in the field of statistics, which consists of inferring that there is a causal relationship between two or more events because a statistical correlation between them has been observed, and that big data studies have helped to generalise in part for the reasons given above (Muller, 2018).
Big data studies often emphasize the variations and slides that occur in large volumes of data and assign simple explanations to the complex phenomena behind those variations (Michael & Miller, 2013;Poel, Meyer, & Schroeder, 2018;Brady, 2019). One example is studies at the level of the education system, such as those that analyse the segregation of students in neighbourhoods according to socioeconomic level (Ball, Bowe, & Gewirtz, 1995;Orfield & Lee, 2005), or those that make comparisons between academic performance and other geographic variables such as the country or region of residence of the students (Coleman, 1966;Sirin, 2005). In the history of education there has been much research that has sought correlations between simple variables in order to respond to complex problems, and these have often been questioned over the years. Currently, access to large data sources has opened the door to new and increasingly creative interpretations that are closer to the theoretical approach that supports the studies than the observed evidence (Hansen & Reich, 2015;Monarrez, 2018). Limited funding and time constraints also lead researchers to find causality between factors where there is only apparent correlation that does not always explain the variance in variables analysed in the studies.

Sample limitations
When a study is based on big data, there is a risk of not sufficiently understanding the value of the underlying sample. In social research, the sample corresponds to the selection of people chosen to represent the population where the conclusions are to be applied. Since often not all of the population is available, you must choose a sample that represents it and is manageable. The study is applied to the sample with the expectation that the conclusions obtained can also be replicated in the whole population.
In the case of big data, the research is usually very extensive and the populations to which the studies are projected are often very large. For example, they may concern all Internet users (González-Bailón, Wang, Rivero, Borge-Holthoefer & Moreno, 2012;Ruths & Pfeffer, 2014;Pfeffer, Mayer & Morstatter, 2018), or in the case of education there may be studies whose findings are intended to apply to all students participating in digital courses, all university students or all schools located in a particular type of neighbourhood (Warnakulasooriya & Black, 2018). As the information available in the massive databases is very numerous, the researcher tends to think that these data are sufficient to represent the population. However, this is not always the case and, if one moves forward without an adequate sample selection, one will be assuming a certain risk. Thus, problems may arise in guaranteeing representativeness and equity when attempting to generalize results to populations that, because they are so broad, are characterized by great heterogeneity.
The lack of representativeness of the sample in the case of massive information sources can be tackled by using selection methods appropriate to the size of the population. This includes using big data also in the previous phases of the study, so that it is possible to segment the large volumes of data available. And, on the other hand, social research is called to imitate experimental sciences and incorporate scales close to 1:1 both in the process of information analysis and in the inference of results, thus expanding the commitment to the social reality which intends to study.

The network structure does not reveal everything
Most big data research uses social network analysis methods. In education, it is common for LMSs to incorporate the feature of displaying network structures created from relationships between students or from their interactions with learning resources in online courses. Social networks analysis tries to know the evolution of the information flows provided by the people who are interacting in a certain context and, for this purpose, it uses graphic representations that show the connections between the nodes that make up the network -which can be people, messages sent to a forum, interactions with a resource, etc.-, filtered by the attributes of those nodes -for example, the subject of a message, the type of interaction, etc.-and according to the weight of the links between those nodes -more or less weight depending on the role of the person sending the message, whether the resource is autonomous or part of a learning sequence, etc.-In many cases, researchers using social network analysis take into consideration the structural properties of the whole network to infer from them other properties of the links between the different nodes. For example, one of the most common practices is to connect the links between the alters -an individual's network consists of an ego representing that individual, and his alter, which are the others to whom that ego is connected-to the properties of the network structure. This is true only under certain strict conditions where bridging relationships between groups of networks would be more likely to be weak links (Onnela et al., 2007). These are technical issues, but they can lead to inaccuracies as the information contained only in the network structure is limited.

A PEDAGOGICAL FRAMEWORK FOR EDUCATIONAL ALGORITHMS
A set of heuristic top-to-bottom dimensions aimed at filling the gap detected in the design of algorithms in the educational context is proposed below (see Table 1). In social sciences, heuristic-based analytical frameworks are associated with dynamic and open assessment methodologies. Their main utility lies in the formulation of simple evidence-based rules that provide a wide margin for the analysis of cases that depend on a large number of variables, helping to limit the high degree of complexity in those cases. Based on these rules, key performative indicators can be proposed that function under the logic of criteria satisfaction. The criteria are considered satisfied if a minimum percentage of achievement associated with the indicator is covered, which makes the analysis process more open and flexible than control methods based on dichotomous criteria such as A/B type (Gigerenzer & Selten, 2002;Sundar & Singh, 2013;Mousavi & Gigerenzer, 2014).
The proposed scheme is based on the principles already presented and also benchmarked the existing frameworks on the appropriate use of AI systems in other non-educational settings (Saurwein, Just, & Latzer, 2015;Caplan, Donovan, Hanson & Matthews, 2018;Bunker & Thabtah, 2019;;Floridi & Cowls, 2019;Jobin, Ienca, & Vayena, 2019;). The aim is to introduce a pedagogical layer in the general rules that guide the design of data-based algorithms (Reif, n.d.), for which dimensions and questions are posed to guide the action, here following the model of Diakopoulos et al. (2017) and US-ACM (2017). Guiding questions Accountability: In education, Algorithms are used to make decisions and allocate learning resources based on large datasets. And algorithmic accountability is the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.
• Are interested audiences informed about the algorithmic decision-making? • Is there a system of internal rules on transparent behaviour? • Are users warned about taking responsibility when interacting with the system? • Are there public measurement criteria for the system's performance? Biases: When algorithms produce unfair results, we refer to them as biased. Algorithmic biases can occur in many ways: by the social context in which an algorithm is created, as a result of technical constraints, or by the way the algorithm is used in practice.
• Is the system design focused on trust?
• Is there a decision review mechanism?
• Is there a system for social/automatic monitoring of bias? • Is there a system for modification in case of bias?
Data provenance: The data within the algorithms are symbiotic with the algorithm itself. So in product design, the data entered into the algorithms determine the characteristics of a product. When data sets are opaque, there is no way to accurately evaluate the results of digital products.
• Is the data properly tagged?
• Is the algorithm trained to discriminate cultural variants in the data? • What data is used to feed the suggestions? • Does the data of others affect the suggestions in particular cases?
Explainability: Ensure that both the algorithmic decisions and the data that drive them can be explained to end users and learning management stakeholders in nontechnical terms.
• Who are the end-users and who are the stakeholders? • What part of the system can be explained to users and stakeholders? • How much of the data sources can be disclosed? • How many of the decisions assumed by the algorithm can be explained?
Fairness: Ensure that algorithmic decisions do not generate discrimination or unfair impacts when different social profile variables are considered.
• Is there control of users who may be favoured over the disadvantaged? • Is there control of potentially harmful effects generated by the mistakes of other users? • Is there control over the context in which the system operates? • Are cultural rules taken into account?

Dimensions
Guiding questions Harmful content: The design of an AIbased product has to consider the type of content that users can add to a repository. It is detrimental not to check and verify whether that content is harmful or not. Policies are required that define the possible damages caused to third parties in terms of containment and actions.
• Is there control of false identities?
• Is there verification of suspicious content? • Is there a social/automatic damage control system? • Is there a protocol against possible damages?
Pedagogical approach: Not only, but mainly, the design and use of educational data-drive algorithms requires a pedagogical approach. This means addressing, at least, essential issues such as the learning theories behind the AI model, attention to the context of the data, and the usefulness of the output to improve learning.
• Have the features of the people involved in the proposal that are of pedagogical interest been properly framed? • What is the educational theory behind the algorithmic decision-making scheme? • Have evidence-based alternatives in the field of learning been considered? • Have the attributes that are of pedagogical interest been adequately contextualized in the data used? Privacy: The data used in educational algorithms come from individuals. They are intimate data, because conversations and social interactions are various forms of intimacy. So the lack of privacy gradients in the design of the algorithms can facilitate harassment and violations of student privacy.
• Have privacy gradients been defined? • Have intimate, personal, social and public spaces been delimited? • Are there mechanisms for user consent?
• Is the user allowed to move between the variations of public and private?
The dimensions and guiding questions of the framework are intended to provide operational shortcuts to educational professionals on how to incorporate a pedagogical approach as well as student sovereignty into the practice of algorithm design. It also aims to focus on the orientation of algorithms to the achievement of student competencies and skills, on the basis that decisions about recommendations and nudges should be guided by pedagogical evidence. All this seeks to foster safer and more inclusive learning spaces and interactions with IA.

DISCUSSION AND FUTURE WORK
The arguments provided in this paper are intended to complement existing evidence in the scientific literature about providing educators with resources to face the introduction of AI in learning spaces. Proposing key methodologies and guidelines grouped in heuristic rules is considered an appropriate way, since this allows for the management of resources in particularly complex situations.
As a non-technical theoretical proposal, the ability to implement the presented framework in practice will depend on further empirical validations referred to in future studies. Thus, the discussion on the construction of the heuristic scheme points to a set of research references on the design of theoretical frameworks and the subsequent empirical validation of rules and constructs.
Another issue with heuristics that can be discussed concerns the so-called consistency of the context. Heuristics are a great contribution when the assumptions on which they are based are sufficiently consistent in the contexts where they are applied. Therefore, the proposed scheme should also be validated in the variety of phases/territories where it is intended to be applied: either in the design of an algorithm, or in the implementation in practice situations, or if it is a technical development context, or one of educational instructional design, etc.
Simple rule frameworks provide shortcuts that assist both the algorithm design process and the use of digital tools in teaching. However, they cannot be directly applied. It is necessary to previously analyse the effective practices of the subjects in the digital spaces, trying to understand their behaviour in a global way. It is assumed that large data sets -either inherently or as a result of their size-do not have direct answers to the most interesting questions. That is why heuristic rule-based approaches advocate simplifying decision-making in complex learning situations, while optimizing the effect by placing the greatest emphasis on analysing the set of actions that produce a given learning.
The next steps in the field of data-driven educational algorithms aim at deepening from a pedagogical perspective the implementation of derived technologies in real educational practice situations, so that the implications of AI in decision making and in the enrichment of learning processes are fully understood. Also, to advance in the analysis of the challenges that AI implies for educational research. And equally, to be open to the validation -both theoretical and empirical-of schemes such as the one proposed here, which serve as a guide for professionals and academics to manage data-driven digital technologies in learning processes. Ball, S. J., Bowe, R., & Gewirtz, S. (1995


Multi-Objective Optimization Method for Posture Prediction of Symmetric Static Lifting Using a Three-Dimensional Human Model

Background: The development of virtual human models has recently gained considerable attention in biomechanical studies intending to design for ergonomics. The computer-based simulations of virtual human models can reduce the time and cost of the design cycle. There is an increasing interest in finding the realistic posture of the human body with applications in prototype design and reduction of injuries in the workplace. Objectives: This paper presents a generic method based on a multi-objective optimization (MOO) for posture prediction of a sagittal-plane lifting task. Methods: Improved biomechanical models are used to formulate the predicted posture as a MOO problem. The lifting task has been defined by seven performance measures that are mathematically represented by the weighted sum of cost functions. Specific weights are assigned for each cost function to predict both stoop and squat type postures. Some inequality constraints have been used to ensure that the virtual human does not assume a completely unrealistic configuration. Results: The method can predict the hand configuration effectively. Simulations reveal that predicting a squat posture requires the minimization of certain objective functions, while these measures are less significant for the prediction of a stooped posture. Conclusions: In this study, a MOO-based posture prediction model with a validation process is presented. We employed a three-dimensional model to evaluate the applicability of using a combination of seven performance measures to the posture prediction of symmetric lifting tasks. Results have been compared with the available empirical data to validate the simulated postures. Furthermore, the assigned weights are obtained for a range of percentiles from 50% male to 90% female according to the postures obtained by 3D SSPPTM software.

Background
The development of virtual human models has recently gained considerable attention in biomechanical studies to design for ergonomics. There is an increasing interest in finding the realistic posture of the human body with application in prototype design and reduction of injuries in the workplace. The computer-based simulations of virtual human models reduces the design cycle time and cost. To model work environments, most biomechanical models require the precise body posture of the worker. Therefore, posture prediction is a significant aspect of the digital human simulation package. This paper presents a generic method based on a multi-objective optimization (MOO) for posture prediction of a sagittal-plane lifting task. Improved biomechanical models have been used to formulate the predicted posture as a multi-objective op-timization problem. The hypothesis is that human performance measures (cost functions) govern the configuration of the body. There are three main methods of posture prediction. The first uses empirical data, statistical regression, and the data obtained from a large number of experiments or simulations of three-dimensional computerbased models (1)(2)(3)(4). However, this cannot be used in engineering design, where each task requires performing thousands of trials for different percentiles of male and female subjects. The second method uses the inverse kinematics approach to determine the joint parameters that provide a desired position (5)(6)(7)(8)(9)(10). However, due to the difficulty of evaluating the Jacobian when the model has many degrees of freedom, this method can only be used for simple models. The latest approach is the optimization-based method that provides computationally effective models for complex systems. Here, various objective functions that rep-resent human performance measures (such as total effort, discomfort) are optimized (11)(12)(13)(14), and it is possible to consider a combination of various cost functions to formulate a MOO problem and predict more accurate postures. This method addresses most of the questions associated with previous methods (15).
Development of biomechanical models that realistically predict the posture of the human body is a challenge for ergonomists. The ideal biomechanical models have certain attributes. First, realism necessitates the model to be three-dimensional. However, for symmetrical planar tasks, two-dimensional methods have been well justified (16)(17)(18). Second, there should be a balance between the complexity of the model and its computation time. Among many tasks studied by the ergonomists, static lifting posture prediction has gained considerable attention. Three main performance measures suggested to affect the postures assumed in lifting tasks are minimum overall effort (19), local effort or fatigue (20,21), and greatest stability (22,23). Other proposed behavioral criteria or objective functions are the minimum potential energy of a system, joint discomfort, and joint displacement (12,13,(24)(25)(26)(27). However, most of the previous studies have difficulty predicting the forearm and upper arm angle accurately. Furthermore, the question of why some people select the squat posture while the others prefer to assume the stoop type postures for the same lifting condition is both poorly understood and studied.

Objectives
The main goal of this study is to predict realistic stooped and squat postures with reasonable accuracy given the nature of actual human joints. We developed an improved biomechanical model to predict human posture for the sagittal-plane lifting task. To deal with the redundancy of the model, a MOO method with some kinematics constraint was used.

Methods
We first introduced a three-dimensional model and then reduced the number of modes in which it can move (degree of freedom (DOF)) to 11 for simulating a symmetric lifting task in the sagittal-plane. Then we presented the MOO formulation of our inverse kinematic problem along with the design variables and constraints.

Model
Our virtual human model is illustrated in Figure 1. The model consists of six branches, including the right and left leg, the spine, the right and left arm, and the head. The spatial skeletal human model was constructed by using the Denavit-Hartenberg (DH) method (28). Each DOF (the z's) represents the relative rotation of two body segments connected by a revolute joint. The assumption is that in the sagittal-plane lifting task, hands and feet move symmetrically, and no out-of-plane movements occur. Therefore, the kinematics joints from the fingers to the waist have seven DOFs. The kinematic joint for the neck has one DOF, and the kinematic joints for the legs and feet have three DOFs. The virtual human model is presented in Figure 2.

Performance Measures
Among various cost functions suggested for formulating the MOO problem of posture prediction, we considered the weighted sum of seven objective functions of total torque (total effort), delta-potential energy, shear force, compression/tension force, discomfort, sight angle, and 2 Ann Mil Health Sci Res. 2020; 18(2):e104283. head torque about the neck. The optimization problem is formulated as the following equation. (1) Where W i is positive weights that show the relative importance of the objective functions. The weights are obtained by fitting the predicted postures to postures assumed by actual subjects. We use the postures obtained by Dysart and Woldstad (23) and Gallagher et al. (29) in the experiment as a reference to obtain the weights. Our preferences can be incorporated to predict both stooped and squat postures. In what follows, a brief description of each human performance measure is presented.

Total Torque
The hypothesis is that the subject assumes a posture that requires minimum total effort. In this criterion, the minimum effort, which is minimized, is defined as the total external torque summed over the joints. To avoid numerical difficulties, we used the sum of the square of moments as follows:

Delta-Potential Energy
This criterion focuses on the change in the potential energy of each segment of the virtual human model. Mathematically, this performance measure presents a weighted summation of movements of different body segments. Here, the weights are the forces of gravity exerted on each part. The final function is suggested to be as follows: Where n = 19 is the number of lumped masses. Compression/tension forces: Considerable amounts of compression/tension forces are applied to the human body joints during the lifting task. To minimize compression forces, the following objective function is suggested. w c/t,i is 10 for L5/S1, neck, and knee joints, while for other joints w c/t,i = 1.

Shear Forces
forces acting perpendicular to the axis of the spine apply a shearing force that tries to slide the components away from their normal axis. If the shear forces are high enough, ligament and disk tears may lead to shear fractures of the vertebrae. To minimize shear forces, the following objective function is suggested. w s,i is 10 for L5/S1, neck, and knee joints, for other joints w s,i = 1.

Discomfort
Subjects prefer to minimize their joint displacements from a neutral position. To indicate the relative importance of each joint, specific weights are assigned to each joint based on its tendency to be activated. In this study, we used the cumulative joint displacement formulation presented by Yang et al. (24) to approximate the results of the lexicographic method. In our study, we select γ = 1 for knee, ankle, and hip joints. For other parameters in the following equation, we use the values suggested in previous studies. (6)

Sight Angle
Subjects always attempt to maintain the sight of the load in the lifting task. Therefore, to quantify this performance measure, we defined the sight angle as the angle of the vector connecting the eye to the target point ( Figure 2). The formulation is as follows: P eye is the position vector of the center of mass of two eyes; P target is the position vector of the target point (load).
Ann Mil Health Sci Res. 2020; 18(2):e104283. 3 → i is the unit vector in the x direction. To avoid discontinuity of the arc cosine term, and to maintain the mathematical properties of equation 7, the following equation is proposed to be minimized as an alternative. (8) f6 (q) = 10 − 10cos θ (q) 2 Head torque about the neck: In analyzing the postures selected by subjects, it became apparent that they are trying to minimize the movement created by the head over the neck. Thus, the following equation is suggested for this performance measure. (9) f7 (q) = (m head gd) 2 Where d is the projected distance between the CM of the head and the neck joint in the x direction.

Design Variables and Constraints
First of all, the end-effector distance constraint has to be met, since the position of the load is fixed for each lifting task. (10) distance = ||P end−ef f ector − Ptarget point||≤ Second, the posture has to be a stable static posture. Furthermore, given the nature of actual human joints, and by analyzing the postures assumed by subjects in previous experiments, four inequality constraints were considered to avoid predicting unreal postures. These constraints are: (a) each joint angle is restricted to lie within predetermined limits suggested by empirical data, (b) The knee must always be located under the shoulder, (c) The ankle must always be located under the elbow. (d) The head must be above the end-effector (load). Constraints a, b, and c ensure that the predicted posture is not unrealistic. The last limit ensures that the virtual human always maintains sight of the load. These constraints are applicable for predicting both stoop and squat type postures.

Results
First, we obtain the assigned weights for each objective function in Equation 1 by the trial and error method and comparing the predicted postures with the ones obtained in the experiment. We aim to minimize the error of the predicted posture. Subsequently, we verify our model and optimization method by predicting new postures and comparing them with empirical data. All the empirical data were obtained from the experiments performed by Dysart and Woldstad (23) and Gallagher et al. (29). In addition, the process of assigning weights and verifying the results was done by comparing the predicted postures with the ones obtained with 3D SSPP TM software (3D SSPP TM software predicts static strength requirements for tasks such as lifts, presses, pushes, and pulls). For each part, we present two sets of weights for predicting both stooped and squat postures.
All optimization problems were solved using a genetic algorithm. In all simulations, the position of the hand and the ankle joint is fixed. Hand positions are specified in terms of the horizontal and vertical distance from the ankle.

Validation with Previous Experiments
In Figure 3A, the predicted posture and the results obtained in the experiment are presented for the hand position of (30 cm, 50 cm), and lifting a weight of 4.6 kg. The weight value for each cost function is shown in Table 1, and by these weights, three other postures were predicted for the hand positions of (30 cm, 120 cm), (Max, 120 cm), (Max, 50 cm) in Figure 3. Max represents the maximum reach distance. As is visible in these figures, the upper arm and forearm angle were predicted with reasonable accuracy. The mean absolute error (MAE) of the posture prediction is also represented in Table 1 (top). The errors were calculated using the following equation.  Table 1 (bottom). Squat postures obtained from human subjects and simulations have been represented in Figure  4. Errors have also been shown in Table 1.

Validation with 3D SSPPTM
To further validate our predicted postures for other populations and load positions, we have compared our simulations with the results obtained from 3D SSPP TM software for a range of percentiles from 50% male to 95% female in Tables 2-5. To increase the accuracy of our prediction method, for each population, a new set of weights is represented. The suggested weights are averaged over 50 cases and are applicable in case of various hand positions (10 cm < X < 80 cm, 10 cm < Y < 130 cm) and lifting loads (10 N to 40 N). In the case of 50% percentile male, predicted postures for four-hand positions have been illustrated in Figure 5.   are also presented along with the stooped type postures in Figure 6.

Discussion
As can be seen in Tables 1-5

Conclusion
We employed a three-dimensional model to evaluate the applicability of using a combination of seven performance measures for posture prediction of symmetric lifting tasks. Two cost functions of head torque over neck and sight angle were first introduced in this paper and appeared to be the essential part of the MOO method for predicting squat postures.


extfieldsofstudy:
Biology
Computer Science

text:
Quantum Approximated Graph Cutting: A Rapid Replacement for T-REMD?

Determining an optimal protein configuration for the employment of protein binding analysis as completed by Temperature based Replica Exchange Molecular Dynamics (T-REMD) is an important process in the accurate depiction of a protein’s behavior in different solvent environments, especially when determining a protein’s top binding sites for use in protein-ligand and protein-protein docking studies. However, the completion of this analysis, which pushes out top binding sites through configurational changes, is an polynomial-state computational problem that can take multiple hours to compute, even on the fastest supercomputers. In this study, we aim to determine if graph cutting provide approximated solutions the MaxCut problem can be used as a method to provide similar results to T-REMD in the determination of top binding sites of Surfactant Protein A (SP-A) for binding analysis. Additionally, we utilize a quantum-hybrid algorithm within Iff Technology’s Polar+ package using an actual quantum processor unit (QPU), an implementation of Polar+ using an emulated QPU, or Quantum Abstract Machine (QAM), on a large scale classical computing device, and an implementation of a classical MaxCut algorithm on a supercomputer in order to determine the types of advantages that can be gained through using a quantum computing device for this problem, or even using quantum algorithms on a classical device. This study found that Polar+ provides a dramatic speedup over a classical implementation of a MaxCut approximation algorithm or the use of GROMACS T-REMD, and produces viable results, in both its QPU and QAM implementations. However, the lack of direct configurational changes carried out onto the structure of SP-A after the use of graph cutting methods produces different final binding results than those produced by GROMACS T-REMD. Thus, further work needs to be completed into translating quantum-based probabilities into configurational changes based on a variety of noise conditions to better determine the accuracy advantage that quantum algorithms and quantum devices can provide in the near future.

Determining the most optimal protein configurations for binding models is one of the 2 most important tasks to calculate before completing protein-protein, protein-ligand, or 3 further inter-protein analysis. The complex surface characteristics of proteins, even in 4 their crystallographic form without solvent interactions as found within Protein Data 5 Bank (PDB) formatted files, means that the complete computation of binding 6 characteristics of entire structures can be anywhere from infeasible [29] to impossible on 7 1/18 classical computers [7] without model simplification or cutting the number of residues 8 involved in modeling based on experimental results. While determining the most 9 optimal residues for binding models is a straightforward process if experimental data 10 exists for binding kinetics of that protein, it is difficult to complete intuitively for 11 proteins of which there is little known experimental binding behavior, as chemical 12 formation kinetics is completed in non-deterministic, polynomial (NP) time on a 13 classical device [7]. 14 To solve this problem, a multitude of bioinformaticians have turned to tools that can 15 complete molecular dynamics simulations of the proteins undergoing thermal relaxation 16 in order to more effectively reveal their best binding sites. While tools such as 17 Rosetta [14] provide this functionality in the form of ab initio modeling of protein 18 energies and create rigid protein models, Schrodinger [21] and GROMACS' 19 Temperature-Replica Exchange Molecular Dynamics (T-REMD) [38] complete this task 20 by completing iterative molecular dynamics simulations of each of the protein 21 characteristics' interactions. 22 However, the completion of these approximated computational models can be quite 23 time consuming and computationally complex themselves [10]. With a maximum 24 GROMACS performance of 304 ns/day achieved across 600 physical cores with 2 GPUs 25 used per core of access [5], the scale of computational resources needed for molecular 26 dynamics simulations for even multi-hour levels of full protein analysis means that 27 GROMACS is still quite impractical to use at scale when analyzing full interactomes or 28 virions [27]. 29 Consequently, there remains a need to complete the protein binding site analysis 30 before docking at the precision level of molecular dynamics with much faster speeds and 31 with lower cost computational systems to dramatically increase the ability to 32 computationally model full interactomes in order to effectively model protein-protein 33 and protein-ligand binding at scale. 34 Topological Analysis of Protein Surfaces for Binding Site 35 Analysis 36 Recently, there has been a growing amount of work focused on finding the minimal and 37 maximal points of protein models through the analysis of protein shapes, or topology, 38 before adding electrostatically-or electrodynamically-based equations for full 39 pre-binding analysis in order to screen for top sites prior to compute-intensive force 40 calculation. The completion of this task on classical computers with clever, third-degree 41 polynomial topological algorithms [6] or by quantum annealing devices that straddle the 42 line between highly optimized classical computing devices and fully quantum 43 computational devices within a first-degree polynomial complexity class [25] has led to a 44 new paradigm within biological modeling that is focused more on topological feature 45 distinction.

46
Due to the realization of the benefits that could be gained by completing topological 47 models to the overall time of computation, it seemed that the abstraction could be 48 taken one step further through the implementation of Maximum Cut, or MaxCut, 49 graphing algorithms [17]. Not only would these algorithms be able to effectively find the 50 minima and maxima on protein surfaces, but they could actively apply a cost function 51 across the interaction points between the different atoms within the protein to more 52 effectively pick the best binding site with regards to electrodynamics and electrostatics 53 as well. But, while there are potential estimations of a potential Maximum Cut that 54 exist in various algorithmic forms, such as Greedy Cut [39] and the 55 Goemans-Williamson algorithm [19], it has not been formally solved at scale, making it 56 potentially infeasible for protein structure analysis. 57

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Quantum Computing in Improving MaxCut's Odds 58 Quantum processors are devices that use the effects of quantum mechanics for methods 59 of information transfer among bit-like devices. Due to this ability for the bits 60 themselves to hold multiple states, quantum computers seem like ideal solver devices for 61 molecular dynamics calculations, as they could represent every electron within a protein. 62 However, current quantum processors are still in their infancy ; the largest 63 superconducting quantum processor that exists as per the writing of this paper is the 64 Google Bristlecone device, with 72 qubits [8]. As 1 qubit is essentially 1 extra large 65 electron [22], it would take hundreds of qubits to model even the smallest of proteins. 66 However, due to their ability to provide superposition and entanglement features on 67 bits, quantum computers have the potential to take exponential scale problems and turn 68 them into polynomial or even log scale problems [4]. As such, they could be devices that 69 can potentially provide massive improvements to a MaxCut problem. Particularly, the 70 quantum approximate optimization algorithm (QAOA) has been found to be a potential 71 solution to this problem [15]. This hybrid quantum-classical algorithm combines the 72 quantum computer's ability to effectively solve exponential problems with a classical 73 cost function to determine the best cuts within a set of quantum bits, and as such, 74 could provide benefits with few qubits if cleverly designed to scale effectively with 75 classical devices. As such, QAOA has been seen to have biological uses in the past, with 76 the team at ProteinQure using QAOA to effectively fold small proteins [16]. Thus, 77 QAOA seems to be a good candidate for potentially replacing T-REMD. For the past eight months, Iff Technologies has been working with academic and startup 81 clients to test its software, Polar+ [33]. Polar+'s protein pruning tool is an 82 implementation of the QAOA algorithm that includes further bioinformatics 83 contextualization. It has been shown to improve the binding outcomes of protein-ligand 84 interactions [34], However, it has not been directly compared to GROMACS T-REMD 85 and Goemans-Williamson implementations at scale until this point.

86
In this paper, Polar+'s protein pruning tool running on a quantum computer 87 connected to a 1-node classical computer is compared in performance to an instance of 88 Polar+'s protein pruning tool running on a quantum abstract machine running on the  The crystallographic coordinates for the SP-A protein structure were determined using 105 Uniprot [13]. Through analysis of this database, the model 5FFR [20] was determined 106 to be the most all-encompassing structural model of SP-A, covering 147 of its amino 107 acids at a resolution of 2.20Å. However, this model did include phosphocholine ligands, 108 which could interfere with direct interrogation of the amino acids that make up the 109 structure of SP-A [20]. Thus, the phosphocholine was removed from the 5FFR model 110 before binding site analysis.

112
For the completion of the T-REMD analysis, GROMACS 5.0.4 was utilized to create a 113 suitable solvent environment, along with a set of temperature and pressure controls, in 114 order to most accurately determine the protein configuration in a binding environment, 115 as completed in similar studies [35]. Being a molecular dynamics software, GROMACS 116 completes sets of multi-axial nearest neighbor calculations for a set of forces for 117 coordinate position and velocities across a number of time steps [9]. First, forces for 118 each molecule within a solute and solvent are calculated using a prescribed set of forces 119 unique to different solvent environments. As this study aims to understand a protein in 120 a neutral solvent environment, three different force models were used: the Assisted  Being the oldest force field used, AMBER [11] has the simplest form, with total 125 potential energy for a macromolecule following a summation between bond energy as an 126 ideal spring, geometrical energy from each angle within the covalent bonding between 127 atoms, torsioning due to bond order, and intra-atomic forces represented as a van der 128 Waals force added to an electrostatic force, wherein f ij represents the Fourier 129 transformation, ij represents the well depth of the atom's location, and other constants 130 represent their respective parts. This study used AMBER99sb.  CHARMM [24] is a force-field model that aims to take OPLS further through the 139 addition of an impropers and a Urey -Bradley term, which intend to improve upon the 140 torsional modeling of the atomic interactions through the accounting of bending and 141 non-binding interactions between atoms in the 1,3 positions of an organic molecule due 142 to proximity of electrostatic forces, respectively. This study used CHARMM36.
Equation 3. CHARMM force field formula adapted from Mackerell et al. for electrical potential across protein [23] The following system has been utilized for the benchmarking of the GROMACS 144 software: 145 SP-A protein in water bulk consisting of ∼62,000 atoms (number of water molecules 146 ∼20,000) in 6.5x6.5x6.5 nm 3 cell.

147
The testing runs were done using the following parameters: 2fs timestep, PME 148 electrostatics, and van der Waals forces truncated at 1.2 nm with corresponding 149 pressure and temperature control. The benchmark runs were typically for 10000 steps 150 (20ps) with/without writing output any trajectory and coordinate files (Note that with 151 no write trajectories and confout slightly increases the performance). For our tests, we 152 use the "-pin on" and "-dlb yes" GROMACS flags, where "-pin on" stops the kernel 153 from moving processes between cores by locking the cores, and allow dynamic load 154 balancing to automatically run when the load imbalance is 5% or more, which is 155 important for inhomogeneous systems. Note that for optimal performance, we also try 156 5/18 mdrun -resethway and -maxh=0.05 options, which corrects the benching results. After 157 these first test runs, the force fields for SP-A were taken into consideration for a total of 158 10 ns, or 5,000,000 time steps, in order to be able to obtain reasonable accuracy with 159 regards to the interaction of SP-A within a water model.    through the use of helium based cooling chambers [31]. Using the qubits and their angular rotations themselves, which is determined after 208 running the algorithm over a set of test runs for a particular use case and determining 209 which results provide the most problem-specific information, as the nodes within the 210 graphs that MaxCut must be performed on, the total energy of each of these operators 211 simply needs to be decomposed to determine to which node the maximum cut value At the end of this process, basis states representing different qubits being cut from 217 the graph were provided at different probability levels. These basis states, which were 218 binary numbers in order to the qubit and flip state of that qubit, were contextualized to 219 refer to the qubit that needed to be cut from the graph; 1s were taken to be qubits that 220 were part of the new graph, and 0s were taken to be qubits that were eliminated. To Equation 7. Calculation of scalar energetic from the aggregation of shape complementarity(SC) for the larger protein, the receptor (Rsc), and the smaller protein, the ligand (Lsc), as well as the desolvation energy for derived through the summation of each contact potential atomic contact point's desolvation energy obtained experimentally from Zhang et al. [40], wherein α and β are scaling ratios used to ensure that S sc and S elec are of the same scale. With regards to the actual implementation for each of the force fields, only 48 cores 272 were available for running 5,000,000 time steps. As such, the performance obtained was 273 less than that observed in the test runs. This led to a fastest observed run time of    Produced SP-A models from each software

289
The newly configured models from GROMACS and cut models from Polar+ and     The reason for this increase in speed is the ability of the quantum processor to 317 utilize superpositions of each state and entanglement between states to produce strong 318 cut probabilities nearly instantaneously for multiple atoms, while classical processing 319 devices must complete these tasks serially for each atom. This allows for a slimmer 320 algorithmic approximation that does not require multi-axial force calculations or higher 321 order functions. More importantly, the steps completed by GROMACS to properly 322 characterize the forces for each atom are additive amongst the atoms and then additive 323 amongst each pair of atoms, creating a computational complexity of O(n 2 ) for each 324 atom [10]. But, this still does not compare to the steps that need to be completed for 325 Goemans-Williamson, which, while having a lower dimensionality requirement, has a 326 13/18 complexity of O(n 2 logn) [28] due to its arccos term. Thus, QAOA is naturally the 327 fastest algorithmic implementation, with an expected performance in the O(logn) 328 regime [15]. 329 Surprisingly, however, the QAM-based Polar+ implementation running on a 330 medium-scale classical device was able to outperform the QPU based implementation. 331 While this means that, in this interpretation, the QPU does not outperform a classical 332 computing device for this task, it does mean that the procedural generation of quantum 333 states on a classical device can provide algorithmic speedups to algorithms within the 334 Bounded Quantum Polynomical, or BQP, regime, in which problems are of greater 335 complexity than those that can be solved within deterministic Polynomial (P) time but 336 lesser complexity than those that require Non-Polynomial deterministic time (NP) [26], 337 thus providing further evidence of a quantum algorithm use case that can, if coupled 338 with an exponential time problem on a quantum device, can provide a definite quantum 339 advantage as raised by Ge and Dunjko [18]. steps were to be used for all atoms. However, the number of time steps was capped at 347 5,000,000 steps, which allowed for a P-time implementation.

348
Binding Accuracy

349
In Table 9, one can see the comparison of main results across all the discussed models. 350 Based on this table, the following points can be ascertained: glycosylation site, is involved in binding from SP-A's side, which can allow for 360 glycosylated bond formation. From literature, it is seen that the C-type lectin domain is 361 responsible for carbohydrate recognition (see Figure 1) and NAG residues provide the 362 glycosylation of Spike protein that are mainly present on the S2 segment, leaving the 363 RBD open for binding to receptor [30]. Therefore, while the binding model produced by 364 graph cutting methods make more sense when looking at the exact binding interaction 365 from the perspective of SP-A, where there is a NAG residue interacting with an 366 N-linked glycosylation site, the binding models produced by T-REMD make more sense 367 from the perspective of SARS-CoV-2, as strongest binding is more likely to take place 368 within the RBD. Spike. However, due to the shape complementarity term of the ZDOCK algorithm (Ssc) 376 creating a scalar based upon the degree of path matching between receptor and ligand 377 14/18 structures and a lack of structural elements in models produced through graph cutting, 378 the Ssc component for all Polar+/Goemans-Williamson models will be much lower than 379 those produced by GROMACS, and cannot be directly compared. binding, which would make the overall RMSD lower across graph-cut models.

398
Through this study, it was found that the operation of a quantum abstract machine 399 (QAM) application of the Quantum Approximate Optimization Algorithm (QAOA) 400 aimed at determining graph cuts within a protein structure in order to find top binding 401 site candidates produced the same top binding sites and was faster than a QPU based 402 version of itself, as well as faster than the Goemans-Williamson algorithm operated on 403 the Bridges supercomputer, and the GROMACS T-REMD system on the JUWELS and 404 Bridges supercomputer due to its superior computational complexity. Moreover, when 405 used to provide binding results using the ZDOCK software, it produced results of 406 chemical merit. However, the models produced by GROMACS for which the same 407 binding test was completed had noticeably different binding results and configurations 408 after this binding site determination step, binding directly to the SARS-CoV-2 Spike S1. 409 For this reason, it is difficult to determine which model is superior without the 410 completion of a biochemical assay to determine the actual method of interaction 411 between the SARS-CoV-2 Spike and SP-A. 412 But, the computational improvements that quantum algorithms can provide in 413 graph cutting is definitely promising in the quest to improve the computational time of 414 complex analysis prior to protein-protein binding. Therefore, QAMs running quantum 415 algorithms do seem to be an interesting area to investigate towards improvements of 416 molecular dynamics simulations, especially with the addition of quantum specific state 417 analysis through the determination of base and excitation energies using the variational 418 quantum eigensolver (VQE) or similar [37] to provide improvements to quantum 419 mechanics based models that can be completed in GROMACS today. 420 Lastly, this study did little to parameterize the qubits for the different thermal 421 environments, which could have provided at par or higher accuracy in the type of 422 modeling generated through graph cutting. Through the introduction of thermal 423 relaxation models [32] to each bond site, the identification of qubits that best represent 424 the electrostatic environment of the atom, the QPU can provide models that can