Discipline Factor and the Impact on Mathematics Learning Outcomes in Vocational High Schools

In general, the aim of the research was to examine the contribution of communication and motivation to Mathematics learning outcomes indirectly through learning discipline. Specifically, the objectives of this study were: 1) to examine the contribution of communication, motivation, and discipline to Mathematics learning outcomes; and 2) to examine the contribution of communication and motivation to the discipline of learning mathematics. The type of the research is correlational quantitative. The populations of the study were 704-grade X students of SMK Negeri 1 Jenang Ponorogo. The research’s samples were 255 students determined by the Slovin formula. The sampling employed proportional random sampling by lottery. The data analysis technique used was path analysis. The results of the study are: 1) interindividual communication, learning motivation, and learning discipline did not contribute simultaneously at α = 0.05 to mathematics learning outcomes; 2) interindividual communication and learning motivation contribute simultaneously to learning discipline at α = 0.05 (with the amount of the contribution of 31.8%; and 3) communication between individuals affects Mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.514. Learning motivation affects Mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.09.

Introduction
Mathematics learning outcomes play an important role in a student's career. This statement is in line with a belief that learning outcomes is related to the quality of human resources and of course has an important role in the progress of a nation [1]. According to [2], learning outcomes are abilities that students have after receiving their learning experiences. Furthermore, learning outcomes can be classified into three, namely: 1) skills and habits, 2) knowledge and understanding, and 3) attitudes and ideals. To achieve and keep pace with these developments, the students can be supported by qualified education. Education is something that everyone must experience either formally or informally. The education obtained can create a better future for the nation's children.

Method
Based on the approach, the type of this research is quantitative research with correlational research design. [10] argued that quantitative research uses numerical data and emphasizes the research process on measuring objective results using statistical analysis. The focus of quantitative research is collecting data and analyzing it and making generalizations to explain specific phenomena experienced by the population. Furthermore, [11] stated that correlational research is a study that involves activities to determine the relationship and level of the relationship between two or more variables. The populations of this study were 703-grade X students of SMK Negeri 1 Jenang Ponorogo of the academic year of 2019/2020. The samples of this study were 255 students. The number of the samples was determined by the Solvin formula, i.e.
Note: n = sample size, N = population size, and e = percent leeway in inaccuracy due to tolerable or desirable sampling errors, 1% -5% [11]. The sampling technique used was proportional random sampling by lottery. The data collection techniques were by using questionnaires and documentation. The questionnaire was used to collect the data on communication, motivation, and learning independence. Questionnaire is defined as a data collection technique in the form of a list of statements/questions given to other people who are willing to respond (respondents) according to the request of the researcher [12]. Before the questionnaire was used, the validity and reliability were tested. The validity of the questionnaire examine to what extent the measurement is precise in measuring what is to be measured, while the reliability questions to what extent a measurement can be trusted because of its consistency [13]. Meanwhile, the documentation method was used to retrieve data on mathematics learning outcomes in the form of the 2019/2020 Odd Semester Final Assessment scores.
The data analysis techniques used was path analysis technique. The analysis process consists of several stages, namely prerequisite tests, path analysis techniques, and hypothesis testing. Path analysis is used to describe and test the relationship model between variables in the form of causation [14]. In this path analysis, there were four variables tested, namely learning communication (X1), learning motivation (X2), learning discipline (Y), and mathematics learning outcomes (Z).

Result and Discussion
Data on communication variables (X1), motivation (X2), and learning discipline (Y) obtained from filling out a questionnaire of 15 items each, as well as data on mathematics learning outcomes obtained from the analysis of the value document from a sample of 255 students are briefly presented in figure 1. Based on the frequency distribution of each research variable, if classified as low, medium, and high, then it is concluded that for the communication variable there are 83 students (32.55%) in the low category, 97 students (38.04%) in the moderate category, and 75 students (29.41%) in the high category. Meanwhile, the motivation variable shows that 77 students (30.19%) are in the low category, 102 students (40%) are in the medium category, and 76 students (29.81%) are in the high category. In discipline variable, there are 84 students (32.94%) in the low category, 88 students (34.51%) in the medium category, and 83 students (32.55%) in the high category. Then, the learning outcomes 5th PROFUNEDU (ALPTK-PTM) 2020 Journal of Physics: Conference Series 1720 (2021) 012003 IOP Publishing doi:10.1088/1742-6596/1720/1/012003 4 variable contained 81 students (31.76%) in the low category, 105 students (41.18%) in the medium category, and 69 students (27.06%) in the high category. Based on the five aforementioned variables, the average level of the sample students of SMK Negeri 1 Jenang Ponorogo is moderate. This is different from the research by [15] which indicated that the level of achievement of class VIII students of SMP Negeri 26 Satu Atap Pallantikang was in the low category. This difference may occur due to the different levels of school units.
Prior to the path analysis, the data that have been obtained were subjected to a prerequisite test.  [16], to be free from multicollinearity problems, a tolerance value must be higher than (>)0.10 and a VIF value must be lower than (<)10. It can be concluded that the data of this study did not have multicollinearity symptoms in the regression model.
The result of the heteroscedasticity test shows that the significance value of the communication variable on discipline is 0.928 and the motivation variable for discipline is 0.280. Both of these variables have a significance value above 0.05, so there are no symptoms of heteroscedasticity for these variables on the discipline variable. For the communication variable on learning outcomes, it has a significance value of 0.842; 0.729 for the motivation variable on learning outcomes; and 0.832 for the discipline variable on learning outcomes. The three variables have a significance value above 0.05, so these variables have no symptoms of heteroscedasticity on learning outcomes.
In the autocorrelation test, the Durbin-Watson value for communication and motivation variables for discipline is 1.865. For communication, motivation, and discipline variables on learning outcomes, the Durbin-Watson value is 1.988. The two values are more than the DL value and less than the 4-DU value, so the data in this study are not autocorrelated.
After the prerequisite test was met, a path analysis was then carried out. The results of the path analysis in the form of the amount of contribution between variables through the coefficient value (ρ) on each path are presented in Figure 2.  168. This shows that Fvalue < Ftable. It can be concluded that H0 is accepted. In other words, communication, motivation, and discipline do not contribute simultaneously towards mathematics learning outcomes with α = 0.05. Communication, motivation and discipline altogether cannot improve mathematics learning outcomes. The results of this study are not in line with the results of [17] study which stated that there was a significant positive relationship between student's learning motivation and mathematics learning outcomes. Likewise, it is not in line with the results of [18] which concluded that there was a positive effect of discipline on student's achievement. This difference occurs because the research conducted has several limitations. The limitations of this research include, for example, vocational high school students and the filling of the instruments are not strictly controlled. The tendency of vocational school students to dislike mathematics makes it possible that in filling out the questionnaire, they did not have full concentration.
The second hypothesis test obtained Fvalue of 58.868 with F (0.025; 3; 251) of 3.168. In other words, the Fvalue > Ftable; which indicates that the H0 is rejected. It means that communication and motivation contribute simultaneously to discipline with α = 0.05. The results of this study are in line with the results of research by [8] which concluded that motivation had a partial effect on student discipline with a significance value of 0.017<0.05. Because H0 is rejected, it can be continued with a partial test using the t-test. The partial test results are presented in table 1.  [19] study which stated that interpersonal communication has a direct effect on work attitudes, meaning that increased interpersonal communication causes an increase in teacher work attitudes. Likewise, the research results of [20] concluded, among other things, there was an effect of student's discipline on student achievement of SMA/MA students in Mataram City of 7.8%. For the motivation variable (X2), the Fvalue is 1.422 with a significance of 0.156, so H0 is accepted, because the significance value is more than 0.05. It means that partially, motivation does not contribute significantly to discipline. The results of this study are not in line with the results of research by [21] which concluded that there was an effect of work motivation on employee's work discipline in Sukakarya Village, Tarogong Kidul District, Garut Regency. Likewise, it is not in line with the research results of [22], which concluded that, among other things, work motivation had a positive effect on employee discipline of PT. PLN East Java Distribution Malang Area. With the limitations of this study as it is previously stated, it can be interpreted that partially, learning motivation contributes significantly to discipline at the 15.6% significance level.
The results of the path analysis show that the contribution is direct or indirect. Communication variable affects mathematics learning outcomes indirectly through discipline by 0.514. Meanwhile, the motivation variable affects learning outcomes indirectly through discipline by 0.096. The results of this study are supported by the results of the research by [23] & [24], which concludes, among other things: 1) communication has a positive and significant effect on the performance of the employees of the Education Quality Assurance Institute in Central Sulawesi Province; 2) motivation has a positive and significant effect on the performance of the employees of the Central Sulawesi Province Education Quality Assurance Agency; and 3) work discipline has a positive and significant effect on the performance of employees of the Central Sulawesi Province Education Quality Assurance Institute.

Conclusion
Interindividual communication, learning motivation, and learning discipline do not contribute simultaneously at α = 0.05 to mathematics learning outcomes. The cause of the absence of the simultaneous contribution is this study's limitations, namely the samples were vocational school students and the filling out of the instruments was not strictly controlled. The tendency of vocational students to dislike mathematics makes it possible not to have full concentration during the filling out of the instrument. Interindividual communication and learning motivation simultaneously contribute to critical thinking skills at α = 0.05. The amount of contribution of interindividual communication and learning motivation to the discipline of learning mathematics is 31.8%. Communication between individuals affects mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.514. Meanwhile, learning motivation affects mathematics learning outcomes indirectly through learning discipline at α = 0.05, amounting to 0.09.


A literature review on the affective factors that in ﬂ uence data-driven decision-making

Data-driven decision-making (DDDM) has been playing an increasing role in contemporary teaching, since it includes systematic collection, analysis, and application of data to improve students ’ educational performance. However, little is known about the affective factors that in ﬂ uence this data-based practice. Thus, the purpose of this study was to systematically examine previous research on the affective factors that in ﬂ uence DDDM based on the following criteria: (1) the level of DDDM usage; (2) the emphasis of affective factors; and (3) the nature of the interventions and their effects on teachers. According to the ﬁ ndings, this literature review showed how little DDDM-related affective factors have been researched, even though the knowledge of DDDM can help expand its application in the educational ﬁ eld. For example, although the most widely used tool is the Data-Driven Decision-Making Ef ﬁ cacy and Anxiety Inventory (3D-MEA), which has shown promising results in terms of measuring the ef ﬁ cacy and development of data literacy, other affective components have yet to be tested, due to their novelty in the ﬁ eld. The implication of the ﬁ ndings is that obtaining more information about DDDM and its affective elements can help reduce teachers ’ anxiety toward this approach and ultimately enhance the overall educational process.

INTRODUCTION/BACKGROUND
Data-driven decision-making (DDDM) has been playing an increasing role in contemporary teaching, since it includes conscious, systematic data collection, analysis, and application to enhance students' performance and support educational matters (Marsh, Pane, & Hamilton, 2006). For example, DDDM-related tools are used to measure students' diagnostic, formative, and summative knowledge in national competency tests. A similar system is the academically certified eDia (Csap o & Moln ar, 2019). This Hungarian diagnostic support tool provides immediate feedback to teachers about their students' performance, which can be used to enhance teaching, learning, and pedagogical planning. Teachers can also collect the academic results from each subject and compare them with previous ones in order to form their lesson plans accordingly (B us, 2015;Means, Padilla, & Gallagher, 2010). Besides such measuring systems, examinations also have a significant effect on teaching and learning processes. However, although examinations are a natural part of the pedagogical process (Vigh, 2007), DDDM can help create a different approach to measurement data.
In order to implement this in accordance with DDDM, it is important to provide valid and quality data for teachers (Ronka, Geier, & Marciniak, 2010;Sz ell, 2015). It also crucial that teachers have a positive attitude toward data usage and measuring programs, since any anxiety regarding these factors ultimately affect their classroom activities (Firestone et al., 2002;T oth, 2011. However, our knowledge about the affective factors that influence DDDM is scarce. Meanwhile, the application of DDDM in classroom practices is influenced by the different characteristics of teachers. According to Dunn, Airola, Lo, and Garrison (2013), teachers' impressions about their effectiveness and anxiety can be the main factors that strengthen or weaken their dedication toward DDDM.
Provided that teachers perform their tasks in accordance with this approach, it is assumed that they see themselves as efficient in the usage and analysis of measurement information during their work. Conversely, possible reasons behind the lack of systematic usage of measurement information can include: having anxiety when working with data and statistical analyses (Samuel, 2008); feeling inefficient at work or possessing limited training and experience in data analysis, usage, and collection in the given field (Dunn et al., 2013); and working in a nonsupportive institutional environment (Ronka et al., 2010).

PURPOSE
The purpose of this study was to examine previous research on the affective factors that influence DDDM. The main focus was on the affective aspects influencing DDDM (RQ1) in the literature and the measurement tools used to inspect such aspects (RQ2). It is hoped that the findings of this literature review on DDDM-related affective factors can contribute to the understanding and application of DDDM and be used to determine future research directions.

METHODS
In the first phase, DDDM-related books and peer-reviewed articles were collected through electronic databases (e.g., ERIC, Science Direct, and Google Scholar) and filtered based on the condition that they approached the topic empirically. The temporal scope of the literature review was between 2010 and 2020. The following keywords were used: teacher beliefs on DDDM, affective factors of DDDM, engagement to DDDM. In the second phase, the condition was the linkage to practical applications and affective factors. Overall, 25 publications were selected and categorized into two groups: the first group, which focused on the affective elements of DDDM; and the second group, which focused on the empirical analyses of teachers' data literacy and intervention-related affective elements. Moreover, the criteria in this study were as follows: (1) the level of DDDM usage (N 5 8); (2) the emphasis of affective factors (N 5 7); and (3) the nature of the interventions and their effects on teachers (N 5 10).

RESULTS
Based on the systematic review of the DDDM-related literature, a significant aspect was the distinction between teachers' effectiveness and their perceived level of efficacy. In this regard, effectiveness refers to the self-reflective, future-oriented supposition in which individuals possess the required knowledge and ability to successfully perform certain tasks. However, in terms of teachers, it applies to the influential factor on students' educational performance (Bandura, 1997). As for teachers' sense of efficacy, it is possible to obtain a clearer picture about how they evaluate their own DDDM-related efficacy. For example, provided that teachers' perceived sense of efficacy is high, students are positively affected in terms of the results (Dunn et al., 2013). Thus, the higher the DDDM-related sense of efficacy, the more that teachers effectively use DDDM to enhance their teaching and foster their students' knowledge. The effectiveness of DDDM can also be increased through DDDM-related interventions, as shown in previous research on their effects on teachers and teacher trainees (Green, Schmitt-Wilson, & Versland, 2016;Reeves & Chiang, 2018). In the next chapter, I would highlight some researches in systematic order, by presenting some great examples and the most important methods of measuring affective factors of DDDM.

DDDM-related efficacy and anxiety
According to the results, one of the heavily focused areas was teachers' self-perceptions of their data usage and efficiency. In this regard, DDDM-related sense of efficacy refers to the dedication and actions necessary for carrying out successful interventions (Airola & Dunn, 2011). Such efficacy also influences teachers' aims, perseverance, and motivation. According to previous studies, if a teacher believes that he/she will be successful in the implementation of a task, then the task itself might trigger positive emotional reactions, make them more open to try new practices (Straub, 2009), and be more resilient when tackling other task-related problems (Bruce, Esmonde, Ross, Dooki, & Beatty, 2010). In general, the starting point of these efficacy-related studies was teachers' self-perceptions of data usage.
It is also important to note that one of the influencing factors regarding the sense of efficacy was anxiety (Bandura, 1997). In this case, the higher the level of anxiety, the less effective the teacher feels. Meanwhile, the expectation that teachers are required to understand and apply DDDM can also raise their stress level, which, in turn, can increase their feeling of incompetence and negatively affect their application of this approach. However, there are limited resources about the affective aspects of DDDM, even though DDDM-related efficacy and anxiety have a significant influence on its application.
As for teachers' sense of efficacy and anxiety, Dunn et al. (2013) aimed to optimize such aspects by creating the well-established Data-Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA). Before this complex measurement tool was introduced, questionnaires were used as the basis for confirmatory factor analyses (Walker, Reeves, & Smith, 2018) or mapping DDDM-related opinions from teachers and trainees (Green et al., 2016;Price, 2018;Reeves & Chiang, 2017. The overall purpose of Dunn et al.'s (2013) study was to validate the 3D-MEA. In order to implement it, a sample of elementary and high school teachers (n 5 1,728) were asked to complete a questionnaire, the results of which underwent explorative and confirmatory factor analyses. The study confirmed the validity of the questionnaire in which four DDDM efficacy subscales and one anxiety subscale were identified. In this case, the four subscales included data access and detection, technology use, understanding data, and applying data, while the feeling of anxiety itself was one subscale. However, their study did not focus on subject-related differences. The given questionnaire was also used in the study by Price (2018), which focused on the correlation between data-informed school leadership (DISL) and DDDM-related efficacy and anxiety among a sample of teachers (n 5 300). The two main measuring tools in this study were the DISL questionnaire and the 3D-MEA. In particular, the DISL questionnaire included data-related goal setting, development of evidence-based decision-making among teachers, educational programs, and the establishment of a data-rich culture. According to the results, high DISL is related with a high level of DDDM-related efficacy, while there was no significant correlation with DDDMrelated anxiety. Consequently, while the head of institutions tend to consider their work to be data-driven, teachers feel more effective in the field of DDDM.

The effect of DDDM-based interventions on individuals
Based on the literature review in the present study, DDDM-based interventions proved to be a well-researched field. The teachers in the United States who did not participate in special training had limited knowledge about the elements of data literacy (e.g., statistical knowledge, different data systems and their usage, etc.) and relied less on the implementation of DDDM (Means, Chen, DeBarger, & Padilla, 2011;Reeves & Chiang, 2018;Walker et al., 2018). Moreover, there was no strong correlation between their data use and their educational decisions (Dunn et al., 2013).
In related research, DDDM interventions were carried out in different levels of educational systems such as elementary school teachers (Reeves & Chiang, 2018) and teacher trainees (Reeves & Honig, 2015). They also focused on teachers' knowledge of data usage and their abilities and practices, rather than institutions and their learning support. In the present study, it was possible to identify the common conditions of DDDM-based interventions, including their application and effects on individuals as well as their characteristics (e.g., capacity, data properties, and the effects of leadership and organizational culture). Overall, the purpose of the interventions was to develop teachers' DDDM-related knowledge and abilities, and to increase their feeling of satisfaction. In the following, DDDM-related abilities and knowledge are used to determine data literacy (Gummer & Mandinach, 2015).
It is also possible to get an inside view of teachers' perceptions of DDDM and determine how such perceptions change after interventions are performed. In this regard, interventions can enable teachers to gain important knowledge from data that can be used to support certain educational decisions (e.g., differentiation). Nevertheless, the majority of the intervention-focused studies were only based on self-assessment questionnaires and post-training measurement tools (e.g., students' performance), instead of classroom observations and other correlations (Marsh, 2012). For example, some researchers introduced a DDDM system to the participants, performed data collection, analyses, and application training, and administered a satisfaction survey.
In other studies, different types of trainings were found, including one-to two-day trainings (e.g., Green et al., 2016;Reeves & Chiang, 2017), online trainings (e.g., Reeves & Chiang, 2018), and one-year trainings (Van Geel, Keuning, Visscher, & Fox, 2017). For instance, Green et al. (2016) organized data literacy training seminars (n 5 16; 3 3 0.5 days) in order to improve teachers' analysis skills and cooperative practices. University summer camps were also organized (n 5 30) to enhance teachers' data collection skills and statistical knowledge, after which the 3D-MEA and the 3-2-1 formative measuring tool were used. This tool is an activity which encourages participants to reflect on their learnings, giving examples and recognize the unresolved areas. According to the results, the participants eventually recognized the value of the data, especially from the aspect of decision-making in teaching. In the research by Reeves and Chiang (2017), 58 teacher trainees completed a one-day intervention to analyze external data and support their teaching activities based on such data. In this case, the 3D-MEA and a questionnaire regarding the effectiveness of the intervention were used. Overall, the results showed positive changes in the following 3D-MEA subscales: data access and identification, interpretation, and application.
In a related study by Reeves and Chiang (2018), an online data literacy prevention program and changes in the application of teaching materials were monitored. In the latter, there was no significant added value, but a positive change was detected in the DDDM-related sense of efficacy and anxiety among the teacher trainees (n 5 99) and practicing teachers (n 5 25). Moreover, the teacher trainees' attitudes became more positive toward pedagogical evaluations. In this case, the online intervention contained three modules in which the participants received the following information: the aim and the characteristics of the program; the data usage in individual, group, and class levels; and the data usage and grading in class and school levels. Then, they were asked to identify and determine the weak/strong points, the extent of development, and the application in education on the given level. Overall, a total of 7.5 hours was spent in the training. As for the measuring tools, they included the 3D-MEA and a questionnaire about data usage habits, attitudes toward pedagogical evaluations, and the efficiency of the intervention.
In other research, Reeves and Honig (2015) conducted a six-hour intervention among a sample of teacher trainees (n 5 64), after which the data analysis, interpretation, classroom evaluation, and decision-making were measured. The attitude toward data literacy was also a subject of focus. Regarding the measurement tools, they included the Conceptions of Assessment III and Survey of Educator Data Use, which focused on the participants' attitudes and beliefs. Based on the results, increased data literacy and a positive attitude toward data usage was found. In Van Geel et al. (2017) two-year study, they inspected a large sample of teachers (1,182 teachers from 83 schools) and their data literacy. In this case, the measuring tool contained general data literacy-related items and system-specific questions in order to interpret the results. Their study not only found a significant change in these two areas, but it also narrowed the gap between the data literacy of teachers with college and university degrees.
The scope of inspection can be extended to not only practicing teachers' and teacher trainees' intervention programs (Ebbeler, Poortman, Schildkmap, & Pieters, 2016), but also to headmasters and external consultants (Begin, 2018), by using the Data Team Procedure. This procedure is an eight-step, scientific-based process that leads team members (e.g., teachers, headmasters, data experts, etc.) to identify the problems in educational development and make improvements in students' performance. Other problems can include early school leaving, poor academic results in a subject, and decreasing efficiency of an institution. Moreover, this procedure provides data users with the ability to move easily between the various steps of DDDM. According to previous studies, since it builds on the inner capacities of schools, it can be an efficient way to deal with reoccurring problems (Bolhuis, 2017;Schildkamp et al., 2018).
In sum, both short-and long-term intervention programs were primarily used in the literature to focus on DDDM-related efficacy and anxiety as well as the development of data literacy. The 3D-MEA and satisfaction surveys were also used to interpret the training-related efficacy and determine the level of data usage satisfaction.

CONCLUSION
The present study examined the affective aspects influencing DDDM (RQ1) in the literature and the measurement tools used to inspect such aspects (RQ2). For this purpose, the main criteria included: (1) the level of DDDM usage; (2) the emphasis of affective factors; and (3) the nature of the interventions and their effects on teachers. By highlighting the most important researches and methods this literature review also showed how little DDDM-related affective factors have been researched, even though the knowledge of DDDM can help expand its application in the educational field. Self-perception of sense of teacher's work as one of the most influencing factor has been revealed. For example, the most widely used tool to track teachers' DDDM-related sense of efficacy and data literacy was the 3D-MEA. In fact, the teachers with a higher level of efficacy were more willing to use this data-based practice (Dunn et al., 2013). The second affective factor was attitude toward data teachers' own data literacy. This important DDDM-related approach was at interventions. The third one was used to determine their intervention-related satisfaction after pre-and post-evaluations. However, other affective components have yet to be tested, due to their novelty in the field. Thus, obtaining more information about DDDM and its affective elements can help reduce teachers' anxiety toward this approach and ultimately enhance the overall educational process. Moreover, along with measuring the sense of efficacy and anxiety, it would be advisable to conduct studies on teachers' systematic thinking and even motivation toward DDDM application, professional development, and information processes. Mapping these affective elements can also provide a better understanding of the conditions, possibilities, and limits of DDDM application.


extfieldsofstudy:
Economics
Political Science

text:
Towards a more sustainable academic publishing system

Communicating new scientific discoveries is key to human progress. Yet, this endeavor is hindered by monetary restrictions for publishing one's findings and accessing other scientists' reports. This process is further exacerbated by a large portion of publishing media owned by private, for-profit companies that do not reinject academic publishing benefits into the scientific community, in contrast with journals from scientific societies. As the academic world is not exempt from economic crises, new alternatives are necessary to support a fair publishing system for society. After summarizing the general issues of academic publishing today, we present several solutions at the levels of the individual scientist, the scientific community, and the publisher towards more sustainable scientific publishing. By providing a voice to the many scientists who are fundamental protagonists, yet often powerless witnesses, of the academic publishing system, and a roadmap for implementing solutions, this initiative can spark increased awareness and promote shifts towards impactful practices.

Figure 1
The academic publishing process (black arrow path) and solutions for improvements at the individual scientist (green), scientific community (blue), and publisher (purple) levels.

Today's academic publishing system
Academic publishing is the predominant medium scientists use to describe and communicate their findings. It is also the main metric by which scientists are evaluated throughout their career, with frequency of publications and prestige of publishing media as a gauge of scientific achievements. In today's process of academic publishing, scientists provide, often even giving away author copyrights, valuable 1 scientific findings for free to journals, which then sell this knowledge to the public and other scientists (Liedes 1997, Odlyzko 1997, Bachrach 1998, Walker 1998, Smith 2006, Van Noorden 2013, Buranyi 2017. Indeed, most journals charge fees to both the authors for publication of, and to the readers for access to, scientific content, with some allowing higher publication fees to offset the access fee (a.k.a. open access; Smith 2006, Minet 2017, Zhang 2019) and vice versa. Academic publishers also rely heavily on the scientific community for the fastidious tasks 2 of manuscript selection and evaluation (i.e. editorship and peer review), tasks for which solicited scientists also receive no compensation 3 (Odlyzko 1997, Smith 2006, Aarssen and Lortie 2010, Van Noorden 2013, Schmitt 2014, Buranyi 2017, Zhang 2019. As such, any scientist (so-called "peer") can be solicited by a journal for evaluating articles submitted for publication by other scientists in the same field, with scientists contributing massively to editing and reviewing activities on a volunteer basis (Warne 2015, Riley and Jones 2016, Copiello 2018. This volunteer-based system of scientific publishing is not new, and was historically initiated by non-for-profit scientific societies with very small monetary power as a way to strengthen scientific endeavors. However, the academic 1 Scientific findings rely on replicated and technical work which often necessitate time and costly resources. 2 Scientific publications undergo meticulous evaluations by peer scientists acting as journal reviewers and editors, in charge of evaluating the adequacy of the design, execution, and interpretation of the study. 3 The only benefits for such free-of-charge contributions happens when a scientist integrates into the editorial board of a high-profile journal, which can facilitate publishing in that journal and bring some notoriety among peers in the field (Walker 1998, Copiello 2018). 5 publishing industry was quickly overtaken by for-profit companies which prospered on this highly profitable business model, thanks to both the decades of growing public investments in science and globalization of scientific literature (Odlyzko 1997, Walker 1998, Smith 2006, Van Noorden 2013, Schmitt 2014, Larivière et al. 2015, Buranyi 2017, Copiello 2018. This ongoing success-story of the publishing business contrasts profoundly with the dramatic decrease in funding and job opportunities experienced by the scientific community in the current era of austerity, precipitating many scientists into precariousness while being increasingly solicited for free contributions to an ever-expanding publishing industry (Aarssen and Lortie 2010, Schmitt 2014, McDonnell 2016, Herschberg et al. 2018, Ålund et al. 2020. Ironically, the rising competition among scientists resulting from funding scarcity has only exacerbated reliance on existing metrics of research excellency, predominantly based on the number of publications in, and editorship for, a handful of high-profile and often privately-owned, costly journals (Smith 2006, Aarssen and Lortie 2010, Heyman et al. 2016, Magistretti 2016, Buranyi 2017, Minet 2017), further draining scarce scientific resources into the cycle of publication and access fees.
Here we provide a voice to the many scientists who are the fundamental protagonists yet often powerless witnesses of the academic publishing system, and identify several initiatives for stepping away from the current unethical model toward more sustainable academic publishing practices.
Scientific activities rely heavily on funding allocated to individual scientists, research programs, and official institutions, in big part in response to publicly-funded calls for scientific investigation. Yet, paradoxically, dissemination of scientific findings predominantly lay in the hands of few for-profit publishing groups that largely benefit from the commercial exploitation of the knowledge produced by these publicly-funded endeavours (Smith 2006 Neither ethical nor sustainable (Smith 2006), the conflictual scientific publishing system has generated intense debates within many academic institutions and scientific circles, with some recent strong initiatives withdrawing subscriptions to major publishing groups (Smith 2006, Larivière et al. 2015, Buranyi 2017, Minet 2017, Else 2018, Zhang 2019, The Cost of Knowledge), however with no result in a significant shift to a sustainable alternative.

Towards a better publishing model
When the system is drifting astray, individual and community choices can be a driving force towards a better pathway. Most scientists are already aware of the above-mentioned aberrations in the scientific publishing system. However, pushed by the inertia of the ongoing model and without a clear vision for alternatives, it is the path of least resistance to keep fueling conventional publishing practices as authors, reviewers and editors (Whitfield 2012, Heyman et al. 2016, despite this being a clear deviation from the fundamental commitments of the scientific communities for a better society (Odlyzko 1997, Bachrach et al. 1998, Walker 1998). Below we provide cues for an emancipation of the scientific community from the all-for-profit publishing system, and toward a circular and virtuous publishing model.  (Bachrach et al. 1998, Walker 1998. Generalizing the use of non-exclusive and royalty-free publisher licences, an already viable and ongoing system since 2003 (Public Library of Science), will allow authors to reproduce and distribute their findings independently, and encourage the publishers that actually provide additional contributions to the work performed by authors, such as improved content, complementary media design, or outreach support (Odlyzko 1997, Bachrach et al. 1998 From universities and laboratories to manufacturers and providers of scientific equipment, many academic institutions and research societies already use broad advertising, a portion of which can be displayed in specialized scientific publishing media. Advertisement-funded journals could constitute a win-win for profitable businesses that want to advertise products to potential and/or targeted clientele, while reducing burden on scientists in need of publishing at lower costs.
Revolutionizing the academic publishing system inevitably involves several challenges in preserving publishing sustainability and scientific rigor. Many such barriers can be alleviated by changing incentives through steps taken at the individual, community, and publisher levels ( Figure 1). In the present era of transition where old and new academic publishing models coexist, positive initiatives and institutionalized support can promote shifting to a new publishing system that is in line with the scientific commitments for accessible knowledge for the society as a whole. At the end of the road, academic publishers, scientific institutions, and individual scientists all need a durably operating publication system. Engaging collaborative initiatives among these protagonists is key to achieving sustainable publishing practices. Global responses to recent pandemics have shown that open science is possible and necessary (Wellcome 2016(Wellcome , 2020, International Coalition of Library Consortia 2020). Facing the social and environmental challenges of the 21st century (Ripple et al. 2017, Kayal et al. 2019, time has come to make such values a standard, rather than an exception, in the academic system.


Students’ Acceptance of Technology-Mediated Teaching – How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany

In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.

INTRODUCTION
Digital technologies have provided support in diverse policy, business, and societal application areas in the COVID-19 outbreak, such as pandemic management (Radanliev et al., 2020b), corporate communications (Camilleri, 2020), analysis of research data (Radanliev et al., 2020a), and education (Crawford et al., 2020). COVID-19 started as a global infectious disease in the spring of 2020, but the necessary measures to control the virus went beyond treatment and were also directed against its spread. Thus, for months, all interpersonal relationships were characterized by social distancing, and the pandemic raised not only medical but also social, economic and technological issues, among others. Higher education was one domain that the pandemic affected radically (Nuere and de Miguel, 2020;Watermeyer et al., 2020). During the worldwide lockdown, higher educational institutions had to immediately switch their activities from the classroom and the campus to a virtual space, which was the only alternative to a complete incapacity to act (Crawford et al., 2020;Kamarianos et al., 2020;Karalis and Raikou, 2020;Owusu-Fordjour et al., 2020;Shah et al., 2020).
University students represent a generation of digital natives for whom this steady switch from the real to the virtual world should not pose any operational challenge (Carlson, 2005;Berk, 2009;Jones et al., 2010). However, research indicates that students show differences according to discipline, such as subject matter (Biglan, 1973;Neumann, 2001) or facets of digital literacy and competency (Nelson et al., 2011), which should be taken into consideration when developing digital learning environments and approaches. The issue of whether and how teaching and learning differs across disciplines has however long been neglected in academic discourse (Neumann, 2001). Furthermore, as in any field, the successful introduction of technology into existing processes -such as the phenomenon that occurred in the COVID-19 pandemic during the springsummer 2020 semester (or the so-called COVID-19 semester)can only be guaranteed if teachers and students show or develop appropriate attitudes, beliefs, behaviors and habits (Al-alak and Alnawas, 2011;Al-Harbi, 2011).
Starting from the circumstances of the pandemic -a rapid transition to fully technology-mediated teaching for students taking different subjects, with no alternative, accompanied by several months of social isolation -in this paper, we ask: Do the acceptance toward completely technology-mediated teaching differ, depending on the discipline of study?
Did the students' acceptance toward completely technologymediated teaching change over time during the COVID-19 semester?
To address the research questions, we empirically examine the acceptance of technology-mediated teaching by students during the COVID-19 semester in the spring-summer of 2020. We follow the suggestion of Neumann (2001) that "the strong influence of disciplines on [. . .] students' learning" creates the need "disciplines to be subjected to greater systematic study, especially regarding their effect on the quality of teaching and learning in higher education, " and present, analyze and discuss the collected data from 875 responses gathered from students of two disciplines (information systems [IS] and music and arts [M&A]) at four points in time.
For our empirical investigation, we apply an extended version of the Technology Acceptance Model (TAM). Technology acceptance is a main topic in information systems (IS) research, and TAM is a widely used approach to investigating a subject's attitude and adoption behavior, inter alia in university context (Venkatesh and Davis, 2000;Lee et al., 2005;Pituch and Lee, 2006;Al-Azawei et al., 2017). For the purpose of this study, the model allows us to investigate the acceptance of technology-mediated teaching, especially regarding certain aspects (usefulness, ease of use and enjoyment) that are relevant for students. Our goal is to understand not only whether students accept technologymediated teaching but also what key aspects are decisive for the future design of technology-mediated teaching environments. For this reason, we apply the TAM, as well as look beyond the model at the research on the advantages and the disadvantages of technology-mediated teaching and the extended TAM, using three new variables to be able to analyze the construct of perceived usefulness for students during the COVID-19 season in more detail and depth.
This paper is organized as follows: In Section "Theoretical Framework, " we discuss the theoretical foundations of our investigation. The design and the procedure of the study, as well as the measures and data analysis are presented in Section "Materials and Methods." The presentation of the results is the focus of Section "Results." We discuss the results of the analysis in Section "Discussion of the Results" and provide implications for teaching practice and organization, educational technology, and research in Section "Implications for Teaching Practice, Educational Technology and Further Research." We conclude this paper in Section "Conclusion" with a short summary, limitations of the study, and remarks on future studies.

Technology Acceptance Model (TAM)
The TAM is one of the most widely investigated and applied models of technology acceptance. Perceived usefulness (PU) and perceived ease of use (PEOU) are the two decisive variables for a person's attitude (ATT) toward a used technology, which in turn affects the actual system use. PU depicts a person's subjective sensation that the application of a certain technology improves individual work performance, while PEOU measures a person's perception of how much effort the usage of the new technology requires. Both variables are influenced by diverse external variables, such as job relevance, subjective norm or output quality (Venkatesh and Davis, 2000). Davis et al. (1989) adjusted the model by adding a person's behavioral intention (BI) as mediator between ATT and actual system use. Table 1 shows an overview of the research on the TAM in the e-learning context. For the e-learning context, Lee et al. (2005) added perceived enjoyment (PE) as an intrinsic motivator, in addition to PU and PEOU, to TAM constructs. Šumak et al. (2011) conducted a meta-analysis and found that the TAM was the most applied model in e-learning and that the size of the causal effects between individual TAM-related factors depends on the type of user and the type of e-learning technology.
For our study, we adapt the research model of Lee et al. (2005), as presented in Figure 1.
Consistent with the findings of prior studies (cf. Davis et al., 1989;Lee et al., 2005), we expect the relations among the constructs to exhibit significant strength (for the list of the hypotheses, cf. Attachment 1). However, in our discussion, we take into account that the TAM in e-learning is usually researched in cases where blended learning or e-learning is an additional part of face-to-face teaching, whereas in the COVID-19 semester, virtual teaching and learning was Mohammadi, 2015 quality features, perceived ease of use, perceived usefulness on users' intentions, satisfaction, usability toward use of e-learning SEM, path analysis 'Intention" and "user satisfaction" both had positive effects on actual use of e-learning. "System quality" and "information quality" were found to be the primary factors driving users' intentions and satisfaction toward use of e-learning. "Perceived usefulness" mediated the relationship between ease of use and users' intentions Al-Azawei et al., 2017 e-learning self-efficacy, perceived satisfaction, learning styles, perceived usefulness, perceived ease of use, intention to use PLS SEM Highlights the integration of perceived satisfaction and technology acceptance in accordance with psychological traits and learner beliefs. Model achieved an acceptable fit and successfully integrated intention to use (ITU) and perceived satisfaction the only channel used to convey content. We examine the measurement model and the structural model and then compare the results over time and for the two student populations (IS and M&A).
Acceptance Over Time Venkatesh and Davis (2000) tested an extended TAM (TAM2) in four longitudinal studies and introduced experience as a relevant influencing factor that is important for understanding the changes in PU over time, whereby experience in general reflects an opportunity to use a technology and is typically operationalized as the passage of time from an individual's initial use of a technology. Based on the TAM, Venkatesh et al. (2003) developed UTAUT and tested it in a longitudinal field study. Venkatesh et al. (2012) introduced three new constructs to UTAUT, measured users' experience and investigated its influence on the users' acceptance and habits. Davis and Wong (2007) applied TAM2 in an educational context and measured users' experience in relation to the actual student usage (system use) of an e-learning system. They pointed out the complex underlying interactions during e-learning adoption processes and recommended a longitudinal design as appropriate for future studies. Pynoo et al. (2011) applied UTAUT in the educational context to investigate the acceptance of digital learning environments and found differences over time; they also pointed out that the usefulness of digital learning environments should be demonstrated to maximize its use.
In contrast to other studies, our study does not focus on a specific technology but on the experience with the technology-mediated teaching in the COVID-19 context. We expect and show within this context that students gain experience during the semester, which will lead to measurable changes in their acceptance.  Hu et al. (1999) define a set of users' characteristics as one factor that can be used to explain, predict and effectively manage technology acceptance. Biglan (1973) points out the characteristics of academic matter, according to which the strongest differences between the "hard" (e.g., engineering sciences) and the "soft" sciences (e.g., social sciences, educational sciences, and humanities) can be identified. Vo et al. (2020) investigated the effects of blended learning on student learning performance and compared the output of students in hard and soft disciplines. According to their study, students in soft disciplines perform better than their peers in hard disciplines when courses are designed in the blended learning modality. Cameron (2017) identified differences in student engagement between 'humanities' (e.g., M&A) and 'professional fields' students (such as IS). Additionally, teaching experiences are more highly regarded by humanities students than those in hard sciences (Cashin and Downey, 1995). In the context of our study, this is expected to lead to differentiating results when lecturers had to quickly change toward virtual formats based on their diverse levels of experience with technologies. Pike et al. (2012) found that that students' academic majors are significantly related to levels of engagement, which is influenced by their acceptance and learning outcomes. Students of enterprising disciplines are more engaged than artistic discipline students. Students of soft applied knowledge (e.g., M&A) need more intensive practical training than those from the disciplines of hard applied knowledge (e.g., IS) (Neumann et al., 2002). Here might be a major disadvantage for specific groups when virtual teaching is applied for learning.

Differences in Acceptance Between Student Groups
According to the research on the learning characteristics and the learning styles of the Net generation (born after 1980) and the Z generation (millennials), university students at the time of the COVID-19 crisis are digital natives, who can be described as tending toward independence and autonomy in their learning styles, technology savvy, interested in communicating visually and in multimedia and able to move seamlessly between real and virtual worlds (Carlson, 2005;Berk, 2009;Jones et al., 2010).
Despite this, it is also characteristic of this generation to view class as a social opportunity and to crave face-to face social interaction, whereby relationships, in-person conversation, interaction and collaboration are high priorities (Howe, 2000;Carlson, 2005;Ramaley and Zia, 2005). Zheng et al. (2017) investigated lowand high-performing students in an e-learning environment and identified a significant difference in the students' perceived usefulness. Xu and Jaggars (2014) found that the typical student had more difficulty with succeeding in online courses than in face-to-face courses [compare also (Nelson et al., 2011); they also noted a variation across subject areas in terms of online course effectiveness].
To the best of our knowledge, to date, no research has put the TAM in the context of the specific characteristics of a study's subjects. This is where our study can make a contribution, as we have examined two different subject groups: M&A students and IS students.
In summary, we expect differences in the students' attitudes toward virtual learning, according to their academic subject.

Benefits and Disadvantages of E-Learning
The benefits and the disadvantages of technology-mediated teaching and learning became a focal point for university research in the context of the COVID-19 crisis (Kamarianos et al., 2020;Karalis and Raikou, 2020;Owusu-Fordjour et al., 2020;Shah et al., 2020). However, this topic is not new but one of the central research focuses in the context of learning in digital learning environments. Davis and Wong (2007) define e-learning as a global phenomenon for organizations and educational institutions, aiming to enhance students' learning experience and effectiveness in terms of the learning outcome. The benefits of e-learning have been discussed in recent research, but so far, there is no consensus on whether the outputs of e-learning are more effective than those of traditional learning formats (Derouin et al., 2005). The most frequently stated benefits are cost efficiency, flexibility (in terms of time and place), saving time to travel to the learning location, easy access to learning materials, as well as the usefulness of learning materials for a longer period (Welsh et al., 2003;Brown et al., 2006;Hameed et al., 2008;Jefferson and Arnold, 2009;Hill and Wouters, 2010;Al-Qahtani and Higgins, 2013;Becker et al., 2013), or the potential to offer personalized learning according to the learner's specific needs (Berge and Giles, 2006).
On the negative side, technology-mediated learning lacks direct social interaction and a personal touch and has the potential to socially isolate the learner or at least to negatively influence social aspects of learning processes (Gimson and Bell, 2007;Hameed et al., 2008;Al-Qahtani and Higgins, 2013;Becker et al., 2013). Socially isolated learning can negatively influence the development of learners' communication skills, as well as change communication conditions, including the lack of support and feedback using non-verbal cues or by observing the interactions of others, as well as the lack of social and cognitive presence and teacher's involvement (Al-Qahtani and Higgins, 2013). Furthermore, learners are insecure about their learning in the absence of regular contact to the teachers (Al-Qahtani and Higgins, 2013). Technology-mediated teaching and learning requires self-motivation, time management and a focused approach and self-directed learning and organization skills of learners (Hameed et al., 2008;Jefferson and Arnold, 2009). According to Al-Qahtani and Higgins (2013), these requirements arise partly from the conditions of social isolation and lack of direct social interaction, which means that the learner must have a relatively strong motivation to mitigate this effect.
During the lockdown of the universities the expectation was that most of the young students will not have any difficulty in switching to online teaching, which is indeed confirmed by actual findings (e.g., Kamarianos et al., 2020). Shah et al. (2020) point out the numerous and immediately apparent benefits of transferring learning to the virtual world: free exchange of information, access to lectures and presentations at conferences that used to involve considerable travel costs, webinars and online discussions, reduction of time inefficiency associated with travel and increased commitment. Owusu-Fordjour et al. (2020) identify negative effects, e.g., learning at home can be ineffective because of many distractions, no adequate learning environment, or contact with the teacher. Less problems have been found in switching to online teaching, however, on the negative side, technical obstacles as well as lack of communication and cooperation, difficulties to concentrate, too many screen-time, lack of logistical infrastructure, non-physical presence, more workload and the loss of lab courses and the general restriction of social contact have been pointed out as important during the crisis. To the positive characteristics belong the easy participation in class, time savings, home comfort, the possibility to learn, new competences, attending and learning flexibility.

Study Design
We conducted a longitudinal study in four German universities using an online survey to capture students' perceptions of technology-mediated teaching throughout the COVID-19 semesters in 2020. Participants in the study were students from selected courses and programs that have been invited to voluntarily take part in the survey. To identify potential differences between disciplines, we gathered responses from different subjects being taught. We have used from the beginning defined e-mail distribution lists and the group of potential respondents remained the same throughout the study. Students were asked for their agreement to the respective statements on an administered LimeSurvey. One survey was administered at the beginning of the semester in Germany (April), two surveys during the semester (May and June), and a final survey at the end of the semester (July 2020).

Measures
The study focused on two main theoretical constructs: (1) (technology) acceptance of e-learning (see Section "Technology Acceptance Model") and (2) the benefits and disadvantages of e-learning compared with face-to-face or blended learning (see Section "Benefits and Disadvantages of E-Learning"). We relied on pre-tested scales when possible; however, we had to adopt these scales for our study. Furthermore, we collected demographic data and asked open-ended questions to gain deeper insights into students' perceptions over the semester.
Concerning the first group of acceptance measurements, we used related items from former studies in a comparable context. We adopted the measurement scales for PU, PEOU, PE, ATT, and BI from Lee et al. (2005), as the authors had already pre-tested these constructs for e-learning activities and proven their applicability. As in the original constructs, the items were measured using a 7-point Likert scale. Slight modifications were made to fit items to the investigated e-learning context.
To address the benefits and disadvantages of e-learning, the identified factors (see Section "Benefits and Disadvantages of E-Learning") were operationalized through a combination of previous studies and the authors' assessment. As highlighted in the previous chapter, for time flexibility (TF), learning flexibility (LF), and social isolation (SI), the theoretical literature provides several important insights into the factors behind the advantages and disadvantages of technology-mediated teaching environments. Table 2 provides an overview of survey constructs and related measurement items as well as their sources of adoption.

Procedure
To identify differences in students' perceptions over time, we surveyed the same student populations four times during the semester. At University 1, we gathered responses from master's students in IS, while at Universities 2, 3, and 4, we surveyed participants involved in courses that are part of the music and arts curriculum (bachelor, M&A).
We sent a link to the questionnaire throughout the semester and gathered 875 responses, of which 246 (28%) came from IS students and 629 (72%) from M&A students. We gathered 147 responses in April, 319 in May, 269 in June, and 128 in July. Of the responses, 59% (513) were received from women, 35% (310) came from men, and the remaining 62 (6%) specified another sex or provided no information.

Data Analysis
Data preparation and analysis were conducted in R with the Stats package, version 3.6.1. Incorrect encodings and values were filtered manually. Throughout the survey, no questions were designated as mandatory. For model testing, only constructs for which all related items were answered were used. Regression model analysis was used to test the individual models. Regression models were estimated using the ordinary least square (OLS) method. The survey constructs were calculated based on the mean values of the respective items. Given the focus of our study, we employed the students' subject as the control variable in all model constructs (see the section on the differences between student groups). A binary dummy variable indicating the M&A group was used. Table 3 provides descriptive details for the model constructs. The constructs average values varied. The respondents assessed the ease of using technology-mediated teaching and related technologies as relatively high (avg. 5.2, SD = 1.19) and simultaneously stated that learning with digital technologies did not necessary lead to completely socially isolated work (avg. 3.47, SD = 1.57). The students were almost in agreement regarding the benefits of learning flexibility (avg. 4.92) and time flexibility (avg. 5.01).
A comparison of the students' groups revealed that uniformity within the information systems group was larger in almost any of the respective constructs (standard deviation was lower). Moreover, we observed that the agreement was higher for the central model constructs for the group of IS students. Details are discussed in the following sections.

Measurement Validity
To ensure the validity of the measurement constructs, two approaches were used. For the new items regarding the benefits and disadvantages of technology-mediated teaching and learning, we first employed an explorative factor analysis (EFA) to assess their suitability to measure related aspects. Apart from the developed items, we assessed the internal validity for all constructs in the model.
Explorative factor analysis has been applied for the constructs related to technology-mediated teaching and learning validity and reliability. A principal component factor analysis with a maximal likelihood estimation rotation was performed on the collected items. The related nine items were employed in a factor analysis, resulting in three constructs. Factor 1 (time flexibility) comprised two items reported on a 7-point Likert scale that explained 30% of the variance with factor loadings from 0.652 to 0.997. Factor 2 (learning flexibility) comprised two items (instead of the three expected, compare Table 4 for the item deleted after the EFA) reported on a 7-point Likert scale that explained 12% of the variance with factor loadings from 0.573 to 0.678. Factor 3 (social isolation) comprised three items reported on a 7-point Likert scale that explained 26% of the variance, with factor loadings from 0.758 to 0.929. Following the results of the EFA, the factors social isolation and time flexibility matched our developed items for each construct. Concerning learning flexibility, the item related to the video lectures (c.f. Table 4) did not match to a significant extent (0.351) and was dropped accordingly. Lastly, the internal validity was assessed for all constructs. The established group of technology acceptance constructs was only tested for their internal validity through Cronbach's alphas. Table 4 provides an overview of the survey constructs internal validity and the survey items used. Apart from the PU and

RESULTS
The overall results of the structural model test are shown in Figure 2. The model accounts for 65% of the variance in ATT and 54% of the variance in BI. For all the model constructs, the significant factors were identified with the survey data. Table 5 provides an overview of the hypotheses and the related results. With the exception of H1 (PEOU -> PU), all TAM hypotheses could be verified in our sample.

Effects of Benefits and Disadvantages of E-Learning on PU
Two of the items in PU directly address the perception of the benefits or advantages of technology-mediated teaching. The third deals with the direct output of learning, which is related to its perceived effectiveness (cf . Table 4). Thus, we analyzed the data in view of the potential relations between the perceived benefits and disadvantages of technology-mediated teaching and PU. Based on our empirical results, we were interested in identifying the sentiments underlying students' perceptions of the usefulness of technology-mediated teaching. We therefore extended the TAM core model with the three new factors influencing PU, as presented in Figure 3.
Furthermore, we conducted a regression analysis of PU over time, as illustrated in Table 6. The effects of TF, LF, and SI explained 34% of the variance in PU in the model test (Figure 3), as well as up to 35% of the variance in PU over time (Table 6), with a very low explanation rate in May, which was also the only month when SI had a significant effect on PU.

Differences in the Perceptions of IS and M&A Students
To identify the differences between the two student groups, a Kruskal-Wallis test was performed. As a non-parametric test, the approach allowed us to identify differences among our subsamples of different sizes. Overall, all central model constructs vary between the student's subject. Moreover, in general compared with IS students, M&A students have more negative perceptions of almost all model constructs.

Differences in Acceptance Over Time
The differences in the central model constructs were analyzed in terms of variations over time and between subject groups. Figure 3 shows the results for BI over time and between subject groups as generally higher for IS students and indicates further differences over time. For IS students, the analysis results reveal increased BI over time toward the end of the semester. For the FIGURE 2 | TAM test results, including LF, TF, and SI as influencing variables. ** Significant at the 0.01-level and *** significant at the 0.001-level.
Frontiers in Psychology | www.frontiersin.org M&A group, we found similar increased BI over time; a slight decline was identified at the end of the semester.
The same tendency in development over time and in significant (see Table 7) differences between the subject groups was observed with regard to PU, PEOU, and PE (visualized in Figures 4-6, respectively).
As shown in Table 8, the model explains up to 59% of the variance in BI for IS students and up to 52% for M&A students. The effect of PU was not significant for both student groups at the beginning and at the end of semester and remained  constantly non-significant for IS students. For M&A students, PU was very significant in the middle of the semester. The effect of ATT was significant in the beginning and the middle of the semester (June) for IS students but weakened over time. For M&A students, the effect of ATT also varied during the semester, being significant in the second month and at the end of the semester. The strongest and most constant significant effect was found for PE in both groups.

DISCUSSION OF THE RESULTS
In this study, we identified differences in the perceptions of the investigated subject groups and over time.   The first research question (RQ1) could be answered positively: For all constructs of our model, the results show significant differences toward completely technology-mediated teaching depending on the discipline of study. In general, for all constructs, M&A students answer more negatively than IS, which leads to the conclusion that they will not accept (complete) technology-mediated teaching to the same extent as IS students. This supports, inter alia, our theoretical findings, especially the findings of Pike et al. (2012), which emphasized that students' academic majors were significantly related to levels of engagement, which is influenced by their acceptance and learning outcomes. IS students in our study furthermore enjoyed the technology-mediated teaching more, although social isolation was the most negatively indicated by both groups. Neumann (2001) emphasizes a strong influence of disciplines on students' learning and behavior, and Nelson et al. (2011) point out differences in facets of digital literacy and competency. Our findings empirically support disciplinary differences in acceptance of technical-mediated teaching between M&A and IS students. We assume a higher acceptance of IS to be a result of the appropriateness of the medium for the subjects' content and the confidence that the content of their lecture can be conveyed technologically, as well as based on general openness toward technology-mediated teaching. The higher acceptance of virtual classroom format might also be a result of the general tendency for people to adopt familiar formats more easily (cf. Janssen et al., 2009); that is, in the present case, IS students were more familiar with technologies and virtual environments than M&A students, and, thus, possibly influencing the corresponding acceptance.
To answer the second research question (RQ2), we analyzed differences over time and between the two groups. The results show that this research question was also positively answered: The students' attitudes toward completely technology-mediated teaching changed over time during the COVID-19 semester. Especially in the last month of the semester, a decline in all constructs was apparent for the M&A group. One reason for this finding may be that at this time, the loosening of social isolation had begun, and face-to-face teaching was possible again, clearly demonstrating its advantages for this group compared with completely technology-mediated teaching. A reason for M&A students' perception of technology-mediated teaching as much less suitable for conveying their learning content could be the lack of opportunities for laboratories and studios, audience response (in music and theater), and practical work in technology-mediated teaching, which are a main focus of their curriculum. We assume that the type of knowledge imparted in the curriculum is also responsible for these differences.
Further, in the context of RQ2, we measured the effect of PE, ATT, and PU on BI separately for both groups using a regression analysis. Even if both groups did not rate the PU as high, the usefulness of technology-mediated teaching did not significantly affect the intentional behavior for any of the months of the survey. PU seemed to be important only for M&A students in the middle of the semester. This could be explained by the fact that at this time, M&A students had gained enough experience and recognized that the contents of their study cannot be transferred properly enough in a technology-mediated teaching environment. The intrinsic factor -enjoyment -however, has a decisive importance, with the remaining strong influencing effect for both groups. Further, the results show that PE was much lower for M&A students. This should be an object of further investigation focusing on variables influencing enjoyment of technology-mediated teaching. To address the special situation during this empirical study, the attitude toward technologymediated teaching was placed in a close relation to the COVID-19 crisis. The effects on BI over time should therefore also be discussed in the context of the crisis. In the middle of the semester, the BI of both groups was significantly affected by the perception of the students regarding the influence of the crisis on the future digitalization of learning processes as well as on the current semester. At the end of the semester, however, IS and M&A students' responses developed in different directionsthe experiences of IS students reduced this significant effect, although for the second group, the effect remained significant at the end. For this group, the lessons learned during the crisis are also more negative.
Besides the results related to RQ1 and RQ2, we provided results with regard to the TAM, which are in alignment with the results of our main theoretical underlying basis, Lee et al. (2005) as well as Venkatesh and Davis (2000). Significant effects could be confirmed in TAM core constructs. We identified in contrast to prior research an inverse effect of PEOU on PU. This might be explained by the fact that this was conducted during the COVID-19 situation, not voluntary, and we did not question the use of a specific technology, but rather technology-mediated teaching in general.
We identified a change of acceptance over time. Shehzadi et al. (2020) investigated the influence of e-learning of Pakistani public and private university students on their satisfaction in the context of the pandemic. Therein, a positive dependence of students' satisfaction with information and communication technologies, e-service quality, and e-information quality as influencing factors of students' e-learning experience was identified. This implies that specific technologies, the service quality, which is, for example, technological smoothness and a high degree of usability, as well as teacher and teaching characteristics, might also be decisive factors for consideration when (re)designing technology-mediated teaching and learning and, thus, addressing student acceptance.
We extended prior research introducing the new variables LF, TF, and SI to TAM and showed how they influence the PU of technology-mediated teaching learning. The possibilities to learn from home, save travel time, and access (video-recorded) lectures independently of time and place are universal benefits of technology-mediated teaching and learning that have gained importance under the special conditions of the lockdown period. Thus, it is not surprising that LF and TF were positively related to PU. The effect of LF was identified as significant in June ( Table 6). TF had a strong effect during the whole lockdown period. Regarding the perceived disadvantages of technologymediated teaching, our results show that SI had a surprisingly positive effect on PU. This could be explained by the situation of the complete lockdown, without alternatives to learning and direct exchange. There is evidence that the willingness to perceive technology-mediated teaching and learning as equivalent to faceto-face teaching and learning is greatest when offered without alternatives (Mehra and Omidian, 2011). The impact of SI on PU was strongest in the second month of the lockdown. This may be the result of the overall phase characteristics: during the second time, it became clear that the crisis would last longer, but the frustration about the social isolation was not yet too great by comparison.

IMPLICATIONS FOR TEACHING PRACTICE, EDUCATIONAL TECHNOLOGY AND FURTHER RESEARCH
The results of this study on technology acceptance during the virtual COVID-19 semester in Germany are important in both the short and the long term. We point out three areas of implications: teaching practice and organization, educational technology, and research.

Teaching Practice and Organization
Our study was conducted in a situation of immediate switch from physical presence to technology-mediated teaching. The extreme circumstances were a big challenge; however, they provided important evidence about technology-mediated teaching at universities. In the current course of the pandemic, the fallwinter 20/21 semester is equally or at least partially technologymediated. In this respect, the findings can help improve teaching directly, especially regarding the differences in the perceptions of the subjects of study.
The differences between the student groups need to be taken into consideration by the teachers when designing virtual teaching and learning environments and conducting teaching. For example, different formats, such as breakout sessions in smaller groups, could be used. Furthermore, specific sensitization to the advantages or necessity of the formats can be applied or the degree of interactivity within the sessions adjusted. To this end, teachers should develop competencies, not only regarding the use of technical tools but also new didactic and methodological skills. Further, the overlap of technological, pedagogical and content knowledge leads to new kinds of interrelated knowledge (Mishra and Koehler, 2006;Archambault and Crippen, 2009;Schmid et al., 2020), which are gaining importance in the context of teachers' education and professional development. The transfer of knowledge through teaching must not occur in such a way that a single technique implies innovation. It is much more challenging for lecturers to demonstrate their methodological and professional competencies through the use of media in the same way as in face-to-face teaching. The initial experiences during the COVID-19 lockdown have shown both possibilities and limitations. The students' direct feedback is all the more important to better exploit the potential of technology-mediated teaching in the future.
In the long term, not only direct teaching practices but also the organization of the teaching processes at the universities as a whole should be taken into consideration. Customized approaches, which differ in respective share of online and offline teaching and learning formats, should be considered for students of different subjects. Whereas, for example, IS students are more familiar with virtual environments, it is assumed that they are more likely to accept and manage the switch to fully virtual learning formats. By contrast, M&A students who are generally assumed to be less familiar with virtual environments may show less acceptance of related formats. Moreover, the appropriateness of virtual teaching and learning may also generally vary among subjects. The acceptance of virtual learning formats should not be considered as similar for all students simply referring to their age/generation. We argue for a consideration of their familiarity and competences with related technologies as well as their technological affinity, which varies among subjects. Moreover, we surmise that personal interaction may not be fully substituted through virtual formats. Hybrid teaching forms seem to become most promising for the future of learning and teaching at universities (Vladova et al., 2020). Therefore, administrative and organizational changes and reorganization of (well established) practices become necessary. These will involve adjustment and further development of the curriculum, stable and trustful technological infrastructure, organization of learning results assessment, as well as the development of a new culture of technology-mediated teaching, including netiquette, behavioral norms, and standards.

Educational Technology
The differences between the student groups clearly show that the use of technologies and the design of technology-mediated teaching offerings should address the specific needs of different study subjects. At the time of the study, communication platforms such as Zoom, Cisco Webex, or Big Blue Button were mainly used for teaching, as well as Moodle as a learning platform for organization of the teaching process. Against this background, the direct user feedback in our study includes important hints for educational technology (EdTech) companies. Currently, these companies mostly focus on the development of learning courses for individual use, pointing out the role of artificial intelligence (AI) and learning analytics. However, the results show the immense importance of the differences in the field of study during the transfer of knowledge in an academic environment. This can be addressed by short-and long-term solutions and may lead to innovative concepts and products, whereby the role of the teacher remains central for the transfer of specific study content. However, students can acquire different content in a completely self-directed and self-organized way. The curriculum of the two groups in our study can be used, among other things, to identify the subject-specific needs of the students.

Research
In the long term, the effects of LF, TF, and SI should be empirically tested and investigated by further research in a COVID-19 neutral situation. Furthermore, the changes in the TAM constructs over time refer to the influence of experience within the acceptance model in education. Thus, future research should investigate whether this experience can influence students' habits and, through this, their acceptance of face-to-face teaching. This is relevant for the phase of returning to direct face-to-face teaching after the crisis, but much more in the long term as university teaching becomes increasingly technology-mediated.
We also identified implications for further research in the context of knowledge management. The results of the study indicate a relationship between the nature of knowledge transferred during the teaching process and the acceptance of technology-mediated teaching. When the shift to the technologymediated learning environment is considered, the nature of knowledge and how it is transferred comes to the forefront (Vladova et al., 2020). The knowledge management literature points out the critical distinction between tacit knowledge (person-bound) and explicit knowledge (not person-bound) (Polanyi, 1966). Whereas explicit knowledge can be transferred in the context of communication processes with the help of numbers, pictures, or language, tacit knowledge is personal and context-specific (Nonaka and Takeuchi, 1995). Therefore, tacit knowledge is difficult to communicate (Nonaka and Takeuchi, 1995) and can be transferred only partly and by common application and practice. For example, Polanyi (1958, p. 92) posits: "Although the expert (. . .) can indicate their clues and formulate their maxims, they know many more things than they can tell, knowing them only in practice, as instrumental particulars, and not explicitly, as objects." Next, during our data analysis, we found some implications for research on the topic of innovation diffusion (Rogers, 2010), as IS students can probably be described as early adaptors and M&A students as the late majority. IS-students can thus be used as a test audience as well as ambassadors for a new learning technology solution. Thus, they would have a trendsetting role within universities. Thanks to their high acceptance, new technologies can be tried out without fear of resistance and their advantages can be recognized.
We also believe that our study could be of interest in the interdisciplinary research field, especially in the context of digital-mediated team, net, and project work. At this point, the experiences and needs of M&A students are especially important to explore. Experiments as well as surveys on these types of teamwork in the university context can provide necessary information on how technology-mediated teaching should be appropriately designed for this user group. This necessitates scientific collaboration between work psychologists, computer scientists and educators.

CONCLUSION
Although a study of this scale cannot be wholly representative of the entire higher education sector, it has provided views from two different disciplines, that is, M&A as well as IS, on the acceptance of technology-mediated teaching and learning in four universities in Germany. Motivated by the need to understand the underlying drivers of student adoption of digital-mediated learning during the COVID-19 semester, we applied the TAM in a longitudinal study and incorporated three new variables (LF, TF, and SI) influencing PU into the TAM. Furthermore, we identified differences between the subject groups regarding their perceived acceptance of digital-mediated teaching and showed the changes in BI over time for both student groups. We used a validated construct for acceptance. However, as we were aware of the specifics of the situation -social isolation and no alternative to the use of technology -we first tested the hypotheses using our sample.
Our study also has some limitations. First, it was conducted under the special circumstances of complete social isolation in every area of life, which has an influence on the results. Furthermore, we summarized the M&A group in the evaluation without consideration for the differences within it (e.g., music, theater, architecture, visual communication). Given the urgency and the circumstances of the situation of our empirical research context, we furthermore did not have the opportunity to directly examine the organizational situation at the participating universities. However, we included questions about digital platforms and tolls, as well as open-ended questions about students' perceptions of the performance of their teachers. Thus, we addressed organizational and technical issues and their impact on student acceptance. The answers to these questions are not the focus of this paper; however, they will help us to place the model in connection with the specific framework conditions at the universities and to analyze the answers more in depth.
In following up on our data analysis, our future research will especially address the changes on the individual level over time, further data collection in the current semester (fall-winter 20/21), and the analysis of the gathered qualitative data of the answers to the open-ended questions. These efforts will allow us to gain further information on students' perceptions of technologymediated teaching during the semester.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

ETHICS STATEMENT
Ethical approval was not provided for this study on human participants because this was an anonymous survey, we used the own server for data administration. The participants provided their informed consent to participate in this study.

AUTHOR CONTRIBUTIONS
GV developed the idea for this empirical research and was involved in all steps of the study and the manuscript preparation. GV and AU prepared the instrument applied in the empirical study. Both prepared a large part of the introduction, theory, and discussion of the results. GV was mainly responsible for the implications, AU for the conclusion. BB carried out the data analysis and described its procedure and results. NG was actively involved in the implementation of the survey and was responsible for the internal review process. All authors contributed to the article and approved the submitted version.

FUNDING
This work has been partly funded by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 16DII127 ("Deutsches Internet-Institut").