Journal of Computing in Higher Education

As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus’ learning analytics practices than many colleges and universities currently provide. Therefore, in the current text, the authors leverage networked learning to create a networked learning analytics logic model that supports colleges and universities in developing more transparent, ethical, inclusive learning analytics plans. The authors build on their previous learning analytics framework as well as extant learning analytics literature to develop the networked learning analytics logic model. The model offers flexibility that allows for adaptive implementation by institutions that are both new to or already engaging in learning analytics initiatives. We encourage those considering learning analytics to implement the model and disseminate their findings so that the model can evolve to align with the dynamic nature of learning analytics implementations.

This study aims to track college students’ on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students’ behaviors and their EEG signals were recorded from fifty-one learning activities. Analyses have been focused on identifying the determinants of student’s engagement levels and revealing the impacts of behavioral sequences and cognitive sequences on student’s engagement levels. The results show that (1) instructional strategies, classroom behaviors, and cognitive states were significant predictors of students’ on-task rate; (2) the continuity of classroom behaviors improved the on-task rate; and (3) the standard deviations of attention and cognitive load were positively correlated with the on-task rate. This study describes a case of integrating multimodal data analysis in classroom teaching and discusses practical implications for improving classroom teaching.

Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled in institutional data ecosystems, and (increasingly) as part of a data ecology driven by a variety of data interests. The success of learning analytics should therefore be understood in terms of data flows and data interests informing the emerging and mutually constitutive interrelationships and interdependencies between different stakeholders, interests and power relations. This article analyses several selected frameworks to determine the extent to which learning analytics understands itself as a data ecosystem with dynamic interdependencies and interrelationships (human and non-human). Secondly, as learning analytics increasingly becomes part of broader data ecologies, we examine the extent to which learning analytics takes cognizance of the reality, the potential and the risks of being part of a broader data ecology. Finally, this article examines the different data interests vested in learning analytics and critically considers implications for student data sovereignty. The research found that most of the analyzed frameworks understand learning analytics as a data ecosystem, with very little evidence of a broader data ecological understanding. The vast majority of analyzed frameworks consider student data as valuable resource without considering student data ownership and their data rights for self-determination.

The purpose of this study was to investigate learners’ experiences in marketing Massive Open Online Courses (MOOCs). The comments of 255 learners, collected from three top-rated marketing MOOCs, were analyzed with MAXQDA, a content analysis software. The analysis of the 517 meanings (unit of analysis) that emerged from these comments produced five themes and 16 associated categories valued by learners, each comprising several categories as follows: (a) topic and its categories: value, content, difficulty level, knowledge gain, insight increase, and cost effectiveness; (b) instructor and its categories: characteristics, content delivery, and communication; (c) peers and its categories: interaction and evaluation; (d) instructional design and its categories: workload, structuredness, and assessment; and (e) learning resources and its categories: quality and diversity. Among the 517 meanings, 448 were positive and 69 were negative, suggesting that the learners approved of the current practices of teaching and learning in the three marketing MOOCs. Further analyses showed that content delivery in the instructor theme and content and value in the topic theme were of considerable importance from the learners’ perspectives with regard to positive experiences; however, peer evaluation in the peers theme and assessment in the instructional design theme were negatively viewed by the learners. Discussion is provided to interpret the findings.

Although e-Portfolio is acknowledged as one of the powerful pedagogical practices that enhance learning in higher education (HE), not much is known about the types of social media (SM) utilized as e-Portfolios and the benefits for students. This literature analysis, using directed content analysis, aims to explore the above vacuum. The research questions in this study are: (1) In what ways do the SM as e-Portfolios benefit students in HE? (2) To what extent are the benefits of SM as e-Portfolios comparable to those of conventional e-Portfolios? and (3) What are the drawbacks that practitioners and researchers need to be concerned with? Findings indicate that blogs are the most popular SM used as e-Portfolios to support learning, followed by social networking sites and collaborative projects. The study yields 13 advantages and 12 drawbacks when SM is manipulated as e-Portfolios. These findings conclude that the use of SM as e-Portfolios has a great potential in supporting students’ learning and development by providing an environment for them to learn meaningfully from their experiences and engage in critical reflections and dialogues that allow them to gain new knowledge and valuable insights and thus, improve their skills. A pedagogical framework for the planning and implementation of SM as e-Portfolios is suggested based on the findings and aims of the papers that were reviewed.

Open Education Resources (OER) may support students in the Global South, who experience high cost of educational materials. This research aims to understand how students from the Global South adopt OER using the lens of the Technology Acceptance Model (TAM) with the concept of playfulness. In addition, we also explored the effect of cultural factors, such as long-term orientation (LTO) and indulgence versus restraint (IVR) on OER adoption intention. 1527 students from 27 higher education institutions in eight countries in the Global South participated in the study. Multilevel linear modeling was used to test the individual-level and cross-level hypotheses. The results suggested that perceived usefulness (PU), perceived ease of use (PEOU), and playfulness had a significant impact on students’ intention to adopt OER. Furthermore, playfulness also strengthened the relationship between PU and the intention to adopt OER. Although the two cultural factors did not have a direct impact on students’ intention to adopt OER, IVR moderated the relationship between PU and students’ intention to adopt OER. Our research contributes to the theory of technology adoption and culture as well as the practice of OER in the Global South. institutions in eight countries in the Global South participated in the study. Multilevel linear modeling was used to test the individual-level and cross-level hypotheses. The results suggested that perceived usefulness (PU), perceived ease of use (PEOU), and playfulness had a significant impact on students’ intention to adopt OER. Furthermore, playfulness also strengthened the relationship between PU and the intention to adopt OER. Although the two cultural factors did not have a direct impact on students’ intention to adopt OER, IVR moderated the relationship between PU and students’ intention to adopt OER. Our research contributes to the theory of technology adoption and culture as well as the practice of OER in the Global South.

This article maps considerations of inclusiveness and support for students with disabilities by reviewing articles within the field of learning analytics. The study involved a PRISMA-informed systematic review of two popular digital libraries, namely Clarivate’s Web of Science, and Elsevier’s Scopus for peer-reviewed journal articles and conference proceedings. A final corpus of 26 articles was analysed. Findings show that although the field of learning analytics emerged in 2011, none of the studies identified here covered topics of inclusiveness in education before the year of 2016. Screening also shows that learning analytics provides great potential to promote inclusiveness in terms of reducing discrimination, increasing retention among disadvantaged students, and validating particular learning designs for marginalised groups. Gaps in this potential are also identified. The article aims to provide valuable insight into what is known about learning analytics and inclusiveness and contribute knowledge to this particular nascent area for researchers and institutional stakeholders.

Despite the proliferation of massive open online courses (MOOCs) and the impressive levels of enrolment they attract, many participants do not complete these courses. High drop-out has been identified as one of the major problems with existing MOOC formats. Our work addresses two factors relating to non-completion. Firstly, MOOCs require a high degree of self-regulated learning (SRL) skills but most do not adequately develop such skills, thus making them inaccessible in practice to many. Related to this is the inflexibility and passivity of many current MOOC formats, preventing individuals from setting their own learning objectives and directing their own learning. This paper presents preliminary findings from an investigation into MOOC learners’ SRL skills and the relationship to how participants learn. Following a design science methodology, we have developed a novel MOOC platform to support learner choice and to assist participants in defining learning goals and developing individual study paths. This paper describes the architecture of the system and presents findings from a pilot MOOC developed on the platform. Our results indicate that there is a high demand for more flexible, self-directed learning but that MOOC learners exhibit deficiencies in specific SRL skills including help seeking and task strategies. The contextualised nature of SRL skills means that even learners with a strong background of formal education may not deploy the best strategies for MOOC learning. This work is of significance to MOOC development in general as it highlights the need for targeted strategies to encourage SRL in MOOC platforms and innovation.

In this commentary we present an analogy between Johann Wolfgang Von Goethe’s classic poem, The Sorcerer’s Apprentice, and institutional learning analytics. In doing so, we hope to provoke institutions with a simple heuristic when considering their learning analytics initiatives. They might ask themselves, “Are we behaving like the sorcerer’s apprentice?” This would be characterized by initiatives lacking faculty involvement, and we argue that when initiatives fit this pattern, they also lack consideration of their potential hazards, and are likely to fail. We join others in advocating for institutions to, instead, create ecosystems that enable faculty leadership in institutional learning analytics efforts.

Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in Knowledge Building inquiries with facilitation through analytics-supported reflective assessment, while the comparison class (n = 38) pursued Knowledge Building inquiries facilitated by portfolio-supported reflective assessment. This study found that analytics-supported reflective assessment positively and significantly influenced undergraduates’ collective knowledge advancement. Path analysis revealed the mechanisms of analytics-supported reflective assessment for supporting undergraduates’ collective knowledge advancement—the undergraduates’ metacognitive engagement and cognitive engagement influenced each other, further influencing their contribution to collective knowledge advancement and domain understanding. This study holds significant practical implications for fostering students’ knowledge building, inquiry, and metacognition by designing technology-enhanced learning environments as collaborative and metacognitive tools. Additionally, the study offers insights into the processes and mechanisms of reflective assessment, contributing to an understanding of how it enhances students’ development of higher-order skills.

This profiling study deals with the self-regulated learning skills of online learners based on their interaction behaviors on the learning management system. The learners were profiled through their interaction behaviors via cluster analysis. Following a correlational model with the interaction data of learners, the post-test questionnaire data were used to determine self-regulated learning skills scores during the learning process. Regarding the scores, the clusters were named through the prominent interactions of the learners yielding three clusters; actively engaged (Cluster1), assessment-oriented (Cluster2), and passively-oriented (Cluster3), respectively. The profiles in the clusters indicate that assessments were mostly used by the learners in Cluster2, while the frequency of the content tools was high in Cluster1. Surprisingly, some tools such as glossary, survey, and chat did not play a prominent role in discriminating the clusters. Suggestions for future implementations of self-regulated learning and effective online learning in learning management systems are also included.

Information problem solving (IPS) is an important twenty-first century skill, but it is lacking at all age levels. One type of information problem, those of an ill-structured nature that require multiple iterations of (re)defining problems and formulating emerging solutions, can be particularly challenging but have received less attention in the IPS literature. Further, the process of solving such problems often reveals, while simultaneously being impacted by, problem solvers’ epistemic beliefs. Using a self-regulated problem-solving model as an analytic framework and taking advantage of multiple data sources, this study examined college students’ self-regulatory patterns in performing an ill-structured IPS task, and compared the patterns displayed by two groups of students with more and less adaptive epistemic beliefs. Sequential analysis of behavioral data revealed different patterns between the two groups. Think-aloud data, interviews, and students’ IPS products showed three key differences between the two groups: difference in the roles of IPS task instructions, difference in the numbers and triggers of queries, and qualitative difference in iterations between page viewing and writing. The findings yielded important insights into the self-regulatory processes of IPS and the role of epistemic beliefs at different problem-solving stages. Implications are drawn for educators and learning designers for developing IPS in higher education.

In the flipped classroom, students engage in preparatory activities to study the course materials prior to attending teacher-guided sessions. Students’ success in the flipped classroom is directly related to their preparation and students tend to change their preparation activity over time. Few studies have investigated why students change their preparation activity. Therefore, we address this gap by first clustering university students (N = 174) enrolled in a flipped course for the first time based on their preparatory activities at three time points. We identified distinct preparatory activity patterns by computing changes in cluster membership. Next, we compared students’ preparatory activity patterns in course performance, motivation, and self-regulation. The temporal investigation of activity patterns provided important insights into how preparation (or lack thereof) at different phases relates to course performance. Intensive preparation only at the beginning of the course was related to significantly worse course performance whereas preparation only in the middle of the course was related to higher course performance. Students who performed intensively during the course had significantly higher course performance, higher intrinsic motivation at the beginning, and higher self-regulation (in particular, time management) in the middle of the course than students showing lower activity during preparation. Our findings provide important implications for future research and educational practice, particularly for students transitioning to flipped classroom learning for the first time.

Online collaborative discussion (OCD) focuses on promoting individual knowledge inquiry and group knowledge construction through active peer interactions and communications. In practice, it is necessary to explore how different modes of OCD come into play, in which student engagement can function as an evaluating indicator. To identify student engagement in OCD, prior research has identified and categorized various types of student roles. However, although students usually change their engagement during the learning process and across learning occasions, most existing research focuses on examining unchanging student roles or developing roles in similar collaborative activities, which might overlook the probable role transitions brought by engagement changes. To fill this gap, this research proposes an integrated probabilistic clustering approach to detect student roles, role transitions, and fine-grained attributes of transitions across the asynchronous and synchronous OCD modes. The results demonstrate four roles (Knowledge Constructor, Task Follower, Isolated Explorer, and Lurker), four transition categories (Maintenance of inactive participant, Transferring to inactive participant, Maintenance of active participant, and Transferring to active participant), and the code co-occurrence structures of four transition categories. This research deepens the understanding of the complexity of student engagement in online collaborative discussions and offers both analytical and practical implications for improving student engagement.

To explore various stakeholders’ understanding of student privacy and how protections for it are enacted on their campus, we conducted interviews with 27 faculty, instructional designers, and academic librarians at Very High Research Activity universities across the United States. Although there were no interview questions concerning the pandemic, participants noted an increase in awareness of student privacy issues as a result of moving instruction, instructional design, and library services into a completely online environment. Findings show diverse, complex student privacy landscapes on American campuses. Most participants did not perceive themselves as having agency in student privacy decisions on their campuses suggesting that these faculty, instructional designers, and academic librarians can improve communication among themselves and work together. More broadly, the findings suggest that governance structures could be improved to develop a more inclusive culture of student privacy.