How is Learning Fluctuating? FutureLearn MOOCs Fine-Grained Temporal Analysis and Feedback to Teachers

Data-intensive analysis of massive open online courses (MOOCs) is popular. Researchers have been proposing various parameters conducive to analysis and prediction of student behaviour and outcomes in MOOCs, as well as different methods to analyse and use these parameters, ranging from statistics, to NLP, to ML, and even graph analysis. In this paper, we focus on patterns to be extracted, and apply systematic data analysis methods in one of the few genuinely large-scale data collection of 5 MOOCs, spread over 21 runs, on FutureLearn, a UK-based MOOCs provider, that, whilst offering a broad range of courses from many universities, NGOs and other institutions, has been less evaluated, in comparison to, e.g., its American counterparts. We analyse temporal quiz solving patterns; specifically, the less explored issue on how the first number of weeks of data predicts activities in the last weeks; we also address the classical MOOC question on the completion chance. Finally, we discuss the type of feedback a teacher or designer could receive on their MOOCs, in terms of fine-grained analysis of their material, and what personalisation could be provided to a student

EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

Replication In Massive Open Online Course Research Using The Mooc Replication Framework

The purpose of this dissertation was to develop and use a platform that facilitates Massive Open Online Course (MOOC) replication research. Replication and the verification of previously published findings is an essential step in the scientific process. Unfortunately, a replication crisis has long plagued scientific research, affecting even the field of education. As a result, the validity of more and more published findings is coming into question. Research on MOOCs have not been exempt from this. Due to a number of limiting technical barriers, MOOC literature suffers from such issues as contradictory findings between published works and the unconscious skewing of results caused by overfitting to single datasets. The MOOC Replication Framework (MORF) was developed to allow researchers to bypass these technical barriers. Researchers are able to design their own MOOC analyses and have MORF conduct it for them across its massive store of MOOC data. The first study in this dissertation, which describes the work that went into building the platform that would eventually turn into MORF, conducted a feasibility study that aimed to investigate whether the platform was able to perform the tasks it was built for. This was done through the replication of previously published findings within a single dataset. The second study describes the initial architecture of MORF and sought to demonstrate the platform’s scaled feasibility to conduct large-scale replication research. This was done through the execution of a large-scale replication study against data from an entire University’s roster of MOOCs. Finally, the third study highlighted how MORF’s architecture allows for the execution of more than just replication studies. This was done through the execution of a novel research study that sought to analyze the generalizability of predictive models of completion between the countries present in MORF’s expansive dataset—an important issue to address given the massive enrollment numbers of MOOCs from all around the world

The Rise of EdTech Platforms in Higher Education : Mapping Themes from Emerging Critical Literature

There is growing criticism of Big Tech platforms across different sectors of society. There is also increasing scepticism towards seemingly wholesale digitalisation of higher education (HE), largely enabled by platform firms, that followed COVID-19-related emergency online teaching. However, there is a scarcity of critical studies of the multiple, interconnected ways in which HE is affected by the rise of educational technology (EdTech) platforms and their providers. The goal of this chapter is to provide an extensive thematic review of the emerging body of work that takes a critical perspective, and particularly of work that highlights political economy dimensions of ongoing HE platformisation. We identify nine key, interrelated themes in the literature that may also signal structural shifts in HE related to rising platforms and providers. We note two meta-themes, platformisation and learnification, and seven sub-themes: datafication, assetisation, modularisation, crowdification, and peer-to-peering (under the meta-theme platformisation); and unbundling, and skillisation & short-circuiting (under the meta-theme learnification). Finally, we discuss the implications of our review, and propose a critical approach to EdTech provision, considering both negative aspects of ongoing platformisation and the need to preserve the public mission of HE in different contexts.publishedVersionPeer reviewe

Big Data and Technologies of Self

The entry of Big Data into the educational field has generated noticeable binary reactions and a recycling of criticisms already directed at the quantification of reality, datafication in the social sciences, standardization in education, and neoliberalism in the West. This paper reapproaches Big Data’s entry into education from a curriculum studies perspective, which deploys interdisciplinary approaches from philosophy, history, sociology and politics of knowledge and wisdom. The analysis of key definitional debates, binary reactions, and systematization are considered from the point of view of historically shifting technologies of self, as core conditions of possibility for the controversies that emerge when two fields intersect. Specifically, the alliance presumed between self and knowledge, and of both with reality, have long and provincial heritages that contemporary movements such as Big Data seem to reanimate and reconfigure. The paper concludes with consideration of whether Big Data can be understood as a gamechanger in the educational and curriculum fields and if so, on what basis

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

[EN]The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human- Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and datadriven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

On Data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Tesis doctoral en inglés y resumen extendido en español[EN] The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

Basic Psychological Needs Satisfaction, Autonomy Support, and Mindsets as Predictors of Self-Regulation in University Online Learners

Problem In contrast to more traditional learning environments, it can be difficult to see and hear both the instructor and, more crucially, the students when engaging in online education. This has been one of the most common criticisms leveled against online education for a long time. The COVID-19 disruption and transformation of online learning in higher education underlines the fact that variance among online learners in terms of academic success and psychological well-being are determined by the level and quality of self-regulation. What is the degree of self-regulation among American university students who study online because of the COVID-19 pandemic\u27s impact, and what variables might affect or perhaps predict this level of self-regulation? Purpose of Study The purpose of the present study was to test a theoretical model that explains how autonomy support, satisfaction of basic psychological needs, and mindsets predict self-regulation among university online learners in the United States. Based on the model fit and direct effect results of the first research hypothesis, the second research model was developed to examine the mediating effect of basic psychological needs satisfaction on the relationship between autonomy support and self-regulation, and whether mindsets could moderate the indirect effect of basic psychological needs satisfaction on the relationship between autonomy support and self-regulation. To assess the data, structural equation modeling (SEM) was employed. Method This study used quantitative analysis of non-experimental survey data collected via Alchemer. A model-testing design was used to examine a theoretical model which proposed that basic psychological needs satisfaction (autonomy, competency, relatedness), autonomy support, and mindsets predict online learners\u27 self-regulation. 1257 people in all completed the survey. The number of complete and valid participant responses was a sample of 404. Excel, SPSS version 26, Mplus version 8.3 were used for data analysis. Structural equation modeling (SEM) was adopted as the main statistical technique. Results The first research model of this study hypothesized that autonomy support, basic psychological needs satisfaction, and mindsets predict university online learners’ self-regulation. Analysis of the data indicated that the first hypothesized research model fit the data (X2=464.364, df=200, Normed Chi-Square=2.231, CFI=0.925, TLI=0.913, RMSEA=0.057, SRMR=0.053). The path analysis indices of model one suggested that autonomy support positively affected university online learners’ basic psychological needs satisfaction (b=0.82, p\u3c0.001). Basic psychological needs satisfaction positively affected self-regulation (b=0.44, p\u3c0.001) and mindsets positively affected self-regulation (b=0.23, p\u3c0.001). Overall, research model one explained 44.2% variance of online learners\u27 self-regulation. The model fit indices showed that the second hypothesized research model fit the data (X2=378.398, df=146, Normed Chi-Square=2.259, CFI=0.921, TLI=0.908, RMSEA=0.063, SRMR=0.050). A significant mediator effect of basic psychological needs satisfaction was found between autonomy support and self-regulation. The results indicated that the conditional indirect effect of autonomy support on self-regulation via basic psychological needs satisfaction was significant both when the mindsets score was high (which suggests growth mindset orientation) (β=0.216, 95% CI [0.098, 0.316]) and when the mindsets score was low (which suggests fixed mindset orientation) (β=0.150, 95% CI [0.031, 0.250]). Conclusions Applying SEM technique for data analysis, the model fit indices showed that the first hypothesized research model of this study fit the data and explained 44.2% variance of university online learners\u27 self-regulation. The path analysis indices of model one suggests that basic psychological needs satisfaction and mindsets play a predictive role in self-regulation among university online learners whereas autonomy support could not be used as a predictor of self-regulation among university online learners. In addition, the path analysis indices of research model one indicates that autonomy support and basic psychological needs satisfaction could not be used as a predictor of mindsets among university online learners whereas autonomy support could predict basic psychological needs satisfaction as suggested by the theoretical framework. A significant mediator effect of basic psychological needs satisfaction was found between autonomy support and self-regulation. Furthermore, the results of the second research model indicate that the conditional indirect effect of autonomy support on self-regulation via basic psychological needs satisfaction was both significant when the mindsets score was high (which suggests growth mindset orientation) and when the mindsets score was low (which suggests fixed mindset orientation). The difference (though not significant) between these two slopes suggests that the mediation effect of basic psychological needs satisfaction on the relationship between autonomy support and self-regulation was slightly stronger when the mindsets score was higher indicating a growth mindset

Assessing cognitive presence using automated learning analytics methods

With the increasing pace of technological changes in the modern society, there has been a growing interest from educators, business leaders, and policymakers in teaching important higher-order skills which were identified as necessary for thriving in the present-day globalized economy. In this regard, one of the most widely discussed higher order skills is critical thinking, whose importance in shaping problem solving, decision making, and logical thinking has been recognized. Within the domain of distance and online education, the Community of Inquiry (CoI) model provides a pedagogical framework for understanding the critical dimensions of student learning and factors which impact the development of student critical thinking. The CoI model follows the social-constructivist perspective on learning in which learning is seen as happening in both individual minds of learners and through the discourse within the group of learners. Central to the CoI model is the construct of cognitive presence, which captures the student cognitive engagement and the development of critical thinking and deep thinking skills. However, the assessment of cognitive presence is challenging task, particularly given its latent nature and the inherent physical and time separation between students and instructors in distance education settings. One way to address this problem is to make use of the vast amounts of learning data being collected by learning systems. This thesis presents novel methods for understanding and assessing the levels of cognitive presence based on learning analytics techniques and the data collected by learning environments. We first outline a comprehensive model for cognitive presence assessment which builds on the well-established evidence-cantered design (ECD) assessment framework. The proposed assessment model provides a foundation of the thesis, showing how the developed analytical models and their components fit together and how they can be adjusted for new learning contexts. The thesis shows two distinct and complementary analytical methods for assessing students’ cognitive presence and its development. The first method is based on the automated classification of student discussion messages and captures learning as it is observed in the student dialogue. The second analytics method relies on the analysis of log data of students’ use of the learning platform and captures the individual dimension of the learning process. The developed analytics also extend current theoretical understanding of the cognitive presence construct through data-informed operationalization of cognitive presence with different quantitative measures extracted from the student use of online discussions. We also examine methodological challenges of assessing cognitive presence and other forms of cognitive engagement through the analysis of trace data. Finally, with the intent of enabling for the wider adoption of the CoI model for new online learning modalities, the last two chapters examine the use of developed analytics within the context of Massive Open Online Courses (MOOCs). Given the substantial differences between traditional online and MOOC contexts, we first evaluate the suitability of the CoI model for MOOC settings and then assess students’ cognitive presence using the data collected by the MOOC platform. We conclude the thesis with the discussion of practical application and impact of the present work and the directions for the future research