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This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

One of the major factors affecting student learning is feedback. Although the importance of feedback has been recognized in educational institutions, dramatic changes--such as bigger class sizes and a more diverse student population--challenged the provision of effective feedback. In light of these changes, educators have increasingly been using new digital tools to provide student feedback, given the broader adoption and availability of these new technologies. However, despite these efforts, most educators have limited insight into the recipience of their feedback and wonder which students engage with feedback. This problem is referred to as the "feedback gap," which is the difference between the potential and actual use of feedback, preventing educators and instructional designers from understanding feedback recipience among students. In this study, a set of trackable call-to-action (CTA) links were embedded in feedback messages focused on learning processes and self-regulation of learning in one fully online marketing course and one blended bioscience course. These links helped us examine the association between feedback engagement and course success. We also conducted two focus groups with students from one of the courses to further examine student perceptions of feedback messages. Our results across both courses revealed that early engagement with feedback is positively associated with passing the course and that most students considered feedback messages helpful in their learning. Our study also found some interesting demographic differences between students regarding their engagement with the feedback messages. Such insight enables instructors to ask "why" questions, support students' learning, improve feedback processes, and narrow the gap between potential and actual use of feedback. The practical implications of our findings are further discussed.

Technological affordances have shown promising potential in advancing the delivery of corporate learning programmes designed for professional leadership development. However, there is a considerable challenge in evaluating learners' skill acquisition, with most of the past research relying on pre- and post-tests or other forms of self-reports to measure leadership development. In that sense, these approaches measure leadership development before and after the programme, while being inefficient for measuring the development during the learning process. This study collected self-reflection answers from a professional development MOOC that allows learners to express their stepwise learning and reflect on their professional experience on leadership fronts. We developed a novel methodology and an automated system for the evaluation of leadership skills' mastery based on the depth of reflection exhibited during the learning process. We identified four groups of learners based on their course content mastery and explored the differences within groups. The results also highlight relevant insights about instructional design and provide promising avenues for future research.

The success and satisfaction of students with online courses is significantly impacted by the sequencing of learning objectives and activities. Equally critical is designing online degree programs and structuring multiple courses to reduce learners' cognitive load and attain maximum learning success. In its current form, the evaluation of program design is complex and done primarily using stakeholders' perceptions and qualitative methods, which are subjective. Emerging technologies produce enormous amounts of educational data that have the potential to lead to evidence-based practice to reform the design of study programs. Although a few studies adopt such data-driven approaches to evaluate and enhance study programs, remarkably little is known about the sequencing of learning objectives and the influence of their mastery in one course on performance across subsequent courses. This study aims to assess the interdependency of learning objectives from multiple courses in a MOOC-based program by drawing on an interdisciplinary approach intersecting analytic techniques and measurement models. Situated in the context of professional leadership skill development, we ground our findings in leadership theory and how learners acquire skills over time and across varying contexts.

Technology is omnipresent in our rapidly changing digital world. Capitalising on these technologies is paramount for education systems to support a modern workforce. This study presents a review of 10 countries, highlighting how different public education systems have integrated digital technologies for teaching and learning. These education systems were selected based on their high performance and continuous improvement in international assessments. Four main themes emerged as key areas of focus from our analysis: improved and enhanced teaching and learning; digital maturation, including tackling the first and second digital divide; data-driven decision making and evidence-based education; and AI in education. Our findings will help benchmark current approaches and uses of digital teaching and learning, including perceived challenges and opportunities that can be used to inform the development and implementation of digital education policies.

The capacity to foster interpersonal interactions in massive open online courses (MOOCs) has frequently been contested, particularly when learner interactions are limited to MOOC forums. The establishment of social presence--a perceived sense of somebody being present and "real"--is among the strategies to tackle the challenges of online learning and could be applied in MOOCs. Thus far, social presence in MOOCs has been under-researched. Studies that previously examined social presence in MOOCs did not account for the peculiar nature of open online learning. In contrast to the existing work, this study seeks to understand how learners perceive social presence, and the different nuances of social presence in diverse MOOC populations. In particular, we compare perceptions of social presence across the groups of learners with different patterns of forum participation in three edX MOOCs. The findings reveal substantial differences in how learners with varying forum activity perceive social presence. Perceptions of social presence also differed in courses with the varying volume of forum interaction and duration. Finally, learners with sustained forum activity generally reported higher social presence scores that included low affectivity and strong group cohesion perceptions. With this in mind, this study is significant because of the insights in brings to the current body of knowledge around social presence in MOOCs. The study's findings also raise questions about the effectiveness of transferring existing socio-constructivist constructs into the MOOC contexts.

Dual-layer MOOCs are an educational framework designed to create customizable modality pathways through a learning experience. The basic premise is to design two framework choices through a course: one that is instructor centered and the other that is student determined and open. Learners have the option to create their own customized pathway by choosing or combining both modalities as they see fit at any given time in the course. This exploratory mixed-methods study sought to understand the patterns that learners engaged in during a course designed with this pathway framework. The results of the quantitative examination of the course activity are presented, as well as the categories and themes that arose from the qualitative research. The results of the analysis indicate that learners value the ability to choose the pathway that they engage the course in. Additional research is needed to improve the technical and design aspects of the framework.

Background: The COVID-19 outbreak came with an unprecedented opportunity to investigate how the new reality of social distancing and limited international travel will affect the organization of academic conferences. Objectives: Drawing on conceptualization of academic conferences as professional learning spaces, in this study, we examine the factors associated with the perceived value of purely virtual academic conferences and how such perceptions differ between participants from different research fields. The aim was to gain knowledge about factors that should be considered when designing a virtual conference. Methods: Survey data from participants of three different virtual conferences were collected (N = 311). Kendall's rank correlation and X[superscript 2]-analyses were performed. Results and Conclusion: Results show satisfaction with social interaction, the extent to which presentations met participants' topics of interest and the perceived importance of learning and getting an overview on the research topic to be related to the value rating. Researchers from different research fields differ significantly in their opinion about the most appropriate conference format regarding getting an overview on the research topic. For some researchers, virtual participation might be a valuable alternative to attending a conference in person. The study serves as a first attempt to understand how and for which target groups virtual conferences serve as a valuable learning event. Further research on this conference format is needed.

In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph embedding techniques to capture both structural and neighborhood-based features of the co-enrollment network. In keeping with Tinto's model, we find that not only do these embedded representations of students' social network predict their final grade point average (GPA), but also are able to successfully classify students who dropout. Our results show that these embedded representations of a student's social network can achieve F1-scores of up to 0.79 when classifying dropout and explain up to 10% of the variance in student's final GPA. When controlling for a small set of covariates and variables common to the literature, this performance increases to 0.83 and 24%, respectively. Furthermore, the performance of these methods is robust to both changes in their parameterization and to corruption of the underlying social networks. Importantly, this implies that hyperparameters may be selected to reduce the computational demands of this method without loss of predictive power. The novelty of this method, and its ability to identify student dropout, merits further investigation to preemptively identify at-risk students.

Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes.

Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers' role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the Internet. The study reported in this paper examined who fulfilled such an influential role in a particular distributed MOOC--a connectivist course (cMOOC) offered in 2011. Social network analysis was conducted over a socio-technical network of the Twitter-based course interactions, comprising both human course participants and hashtags; where the latter represented technological affordances for scaling course communication. The results of the week-by-week analysis of the network of interactions suggest that the teaching function becomes distributed among influential actors in the network. As the course progressed, both human and technological actors comprising the network subsumed the teaching functions, and exerted influence over the network formation. Regardless, the official course facilitators preserved a high level of influence over the flow of information in the investigated cMOOC.

With widespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data--commonly known as trace data--are readily accessible to researchers. Trace data has been extensively used to calculate time that students spend on different learning activities--typically referred to as time-on-task. These measures are used to build predictive models of student learning in order to understand and improve learning processes. While time-on-task measures have been used in Learning Analytics research, the consequences of their use are not fully described or examined. This paper presents findings from two experiments regarding different time-on-task estimation methods and their influence on research findings. Based on modelling different student performance measures with popular statistical methods in two datasets (one online, one blended), our findings indicate that time-on-task estimation methods play an important role in shaping the final study results, particularly in online settings where the amount of interaction with LMS is typically higher. The primary goal of this paper is to raise awareness and initiate debate on the important issue of time-on-task estimation within the broader learning analytics community. Finally, the paper provides an overview of commonly adopted time-on-task estimation methods in educational and related research fields.

This paper reports on the results of an analysis of the research proposals submitted to the MOOC Research Initiative (MRI) funded by the Gates Foundation and administered by Athabasca University. The goal of MRI was to mobilize researchers to engage into critical interrogation of MOOCs. The submissions--266 in Phase 1, out of which 78 was recommended for resubmission in the extended form in Phase 2, and finally, 28 funded--were analyzed by applying conventional and automated content analysis methods as well as citation network analysis methods. The results revealed the main research themes that could form a framework of the future MOOC research: i) student engagement and learning success, ii) MOOC design and curriculum, iii) self-regulated learning and social learning, iv) social network analysis and networked learning, and v) motivation, attitude and success criteria. The theme of social learning received the greatest interest and had the highest success in attracting funding. The submissions that planned on using learning analytics methods were more successful. The use of mixed methods was by far the most popular. Design-based research methods were also suggested commonly, but the questions about their applicability arose regarding the feasibility to perform multiple iterations in the MOOC context and rather a limited focus on technological support for interventions. The submissions were dominated by the researchers from the field of education (75% of the accepted proposals). Not only was this a possible cause of a complete lack of success of the educational technology innovation theme, but it could be a worrying sign of the fragmentation in the research community and the need to increased efforts towards enhancing interdisciplinarity.

This paper describes doctoral research that focuses on the development of a learning analytics framework for inquiry-based digital learning. Building on the Community of Inquiry model (CoI)--a foundation commonly used in the research and practice of digital learning and teaching--this research builds on the existing body of knowledge in two important ways. First, given that the CoI model requires substantial manual coding of student discourse, its potential for guiding pedagogical interventions are limited. Thus, the first contribution is the development of a learning analytics system that automates this coding process by means of a novel text-classification algorithm that takes into the account the process nature of inquiry-based learning and the specifics of communication through asynchronous discussions. Furthermore, it is equally important to investigate how learning processes unfold over time through student interactions with information, technology, and other course participants. With this in mind, the second contribution of this research focuses on the development of analytical models that provide insight into these important aspects of inquiry-based learning.

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models.

In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools--a data analysis toolbox--for the practice of EDM/LA research.

The field of learning analytics was founded with the goal to harness vast amounts of data about learning collected by the extensive use of technology. After the early formation, the field has now entered the next phase of maturation with a growing community who has an evident impact on research, practice, policy, and decision-making. Although learning analytics is a bricolage field borrowing from many related other disciplines, there is still no systematised model that shows how these different disciplines are pieced together. Existing models and frameworks of learning analytics are valuable in identifying elements and processes of learning analytics, but they insufficiently elaborate on the links with foundational disciplines. With this in mind, this paper proposes a consolidated model of the field of research and practice that is composed of three mutually connected dimensions--theory, design, and data science. The paper defines why and how each of the three dimensions along with their mutual relations is critical for research and practice of learning analytics. Finally, the paper stresses the importance of multi-perspective approaches to learning analytics based on its three core dimensions for a healthy development of the field and a sustainable impact on research and practice.

One of the striking differences between massive open online courses (MOOCs) and previous innovations in the education technology field is the unprecedented interest and involvement of the general public. As MOOCs address pressing problems in higher education and the broader educational practice, awareness of the general public debate around MOOCs is essential. Understanding the public discourse around MOOCs can provide insights into important social and public problems, thus enabling the MOOC research community to better focus their research endeavors. While there have been some reports looking at the state of the MOOC-related research, the analysis of the public debate surrounding MOOCs is still largely missing. In this paper, we present the results of a study that looked at the content of the public discourse related to MOOCs. We identified the most important themes and topics in MOOC-related mainstream news reports. Our results indicate that coverage of MOOCs in public media is rapidly decreasing: by the middle of 2014, it decreased by almost 50% from the highest activity during 2013. In addition, the focus of those discussions is also changing. While the majority of discussions during 2012 and 2013 were focused on MOOC providers, the announcements of their partnerships, and million dollar investments, the current focus of MOOC discourse seems to be moving toward more productive topics focused on the overall position of MOOCs in the global educational landscape. Among different topics that this study discovered, government-related issues and the use of data and analytics are some of the topics that seem to be growing in popularity during the first half of 2014.