Search

This study examined the extent to which instructional conditions influence the prediction of academic success in nine undergraduate courses offered in a blended learning model (n = 4134). The study illustrates the differences in predictive power and significant predictors between course-specific models and generalized predictive models. The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts. The differences in technology use, especially those related to whether and how learners use the learning management system, require consideration before the log-data can be merged to create a generalized model for predicting academic success. A lack of attention to instructional conditions can lead to an over or under estimation of the effects of LMS features on students' academic success. These findings have broader implications for institutions seeking generalized and portable models for identifying students at risk of academic failure. [ABSTRACT FROM AUTHOR]

This paper describes a study that looked at the effects of different technology-use profiles on educational experience within communities of inquiry, and how they are related to the students' levels of cognitive presence in asynchronous online discussions. Through clustering of students (N = 81) in a graduate distance education engineering course, we identified six different profiles: 1) task-focused users, 2) content-focused no-users, 3) no-users, 4) highly intensive users, 5) content-focused intensive users, and 6) socially-focused intensive users. Identified profiles significantly differ in terms of their use of learning platform and their levels of cognitive presence, with large effect sizes of 0.54 and 0.19 multivariate η 2 , respectively. Given that several profiles are associated with higher levels of cognitive presence, our results suggest multiple ways for students to be successful within communities of inquiry. Our results also emphasize a need for a different instructional support and pedagogical interventions for different technology-use profiles. [ABSTRACT FROM AUTHOR]

This paper describes a study that looked at the effects of different teaching presence approaches in communities of inquiry, and ways in which student–student online discussions with high levels of cognitive presence can be designed. Specifically, this paper proposes that high-levels of cognitive presence can be facilitated in online courses, based on the community of inquiry model, by building upon existing research in i) self-regulated learning through externally-facilitated regulation scaffolding and ii) computer-supported collaborative learning through role assignment. We conducted a quasi-experimental study in a fully-online course ( N = 82) using six offerings of the course. After performing a quantitative content analysis of online discussion transcripts, a multilevel linear modeling analysis showed the significant positive effects of both externally-facilitated regulation scaffolding and role assignment on the level of cognitive presence. Specifically, the results showed that externally-facilitated regulation scaffolding had a higher effect on cognitive presence than extrinsically induced motivation through grades. The results showed the effectiveness of role assignment to facilitate a high-level of cognitive presence. More importantly, the results showed a significant effect of the interaction between externally-facilitated regulation scaffolding and role assignment on cognitive presence. The paper concludes with a discussion of practical and theoretical implications. [ABSTRACT FROM AUTHOR]

Existing studies have shed light on policies and strategies for learning analytics (LA) adoption, yet there is limited understanding of associations among factors that influence adoption processes or the change in priorities when institutional experience with LA increases. This paper addresses this gap by presenting a study based on interviews with institutional leaders from 27 European higher education institutions. Results showed that experienced institutions demonstrated more interest in exploring learning behaviour and pedagogical reformation than simply measuring a phenomenon. Experienced institutions also paid more attention to methodological approaches to LA than data constraints, and demonstrated a broader involvement of teachers and students. This paper also identifies inter-related connections between prevailing challenges that impede the scaling of LA. Based on the results, we suggest regular evaluations of LA adoption to ensure the alignment of strategy and desired changes. We also identify three areas that require particular attention when forming short-term goals for LA at different phases of adoption • Connections of key adoption factors vary among institutions with different LA experience. • Experienced institutions prioritised exploring over measuring a learning phenomenon. • Experienced institutions were more concerned with methods than constraints of data. • Experienced institutions engaged primary stakeholders more equally. • Special attention is need for institutional context, people issues, and ethics and privacy. [ABSTRACT FROM AUTHOR]

Abstract This paper examines the discrete learning strategies employed within a massive open online course and their relationship to the student learning experience. The theoretical framework centered on the Community of Inquiry model of online education, which outlines the three critical dimensions (presences) of student learning experience: teaching, social, and cognitive presence. The Community of Inquiry survey instrument, administered as the part of the post-course survey, was used to measure student perceived levels of the three presences. Cluster analysis revealed three different groups of students with unique study strategies: limited users, selective users, and broad users. The strategies adopted significantly differed in student use of available tools and resources, final course grade, as well as the perceived levels of cognitive presence. The results also indicate there were significant differences regarding student commitment to learning, motivations and goals for enrolling in a MOOC, as well as goal orientation, approaches to learning, and the use of different study strategies. Implications for research and practice of online learning are further discussed. Highlights • We examined student study strategies within MOOCs based on interaction log data. • Cluster analysis revealed three groups: limited, selective, and broad users. • Differences in final grades and pre- and post-course surveys answers were examined. • We observed differences in final grades and perceived levels of cognitive presence. • We observed differences across factors such as motivation and goal orientation. [ABSTRACT FROM AUTHOR]

Connectivist pedagogies are geared towards building a network of learners that actively employ technologies to establish interpersonal connections in open online settings. In this context, as course participants increasingly establish interpersonal relationships among peers they have greater opportunity to draw on and leverage the latent social capital that resides in such a distributed learning environment. However, to date there have been a limited number of studies exploring how learners build their social capital in open large-scale courses. To inform the facilitation of learner networks in open online settings and beyond, this study analyzed factors associated with how learners accumulate social capital in the form of learner connections over time. The study was conducted in two massive open online course offerings (Connectivism and Connective Knowledge) that were designed on the principles of connectivist pedagogy and that made use of data about social interaction from Twitter, blogs, and Facebook. For this purpose, linear mixed modeling was used to understand the associations between learner social capital, linguistic and discourse patterns, media used for interaction, as well as the time in the course when interaction took place. The results highlight the association between the language used by the learners and the creation of ties between them. Analyses on the accumulation of connections over time have implications for the pedagogical choices that would be expected to help learners leverage access to potential social capital in a networked context. [ABSTRACT FROM AUTHOR]

Research has repeatedly demonstrated that students with effective learning strategies are more likely to have better academic achievement. Existing research has mostly focused on a single course or two, while longitudinal studies remain scarce. The present study examines the longitudinal sequence of students' strategies, their succession, consistency, temporal unfolding, and whether students tend to retain or adapt strategies between courses. We use a large dataset of online traces from 135 students who completed 10 successive courses (i.e., 1350 course enrollments) in a higher education program. The methods used in this study have shown the feasibility of using trace data recorded by learning management systems to unobtrusively trace and model the longitudinal learning strategies across a program. We identified three program-level strategy trajectories: a stable and intense trajectory related to deep learning where students used diverse strategies and scored the highest grades; a fluctuating interactive trajectory, where students focused on course requirements, scored average grades, and were relatively fluctuating; and a light trajectory related to surface learning where students invested the least effort, scored the lowest grades, and had a relatively stable pathway. Students who were intensely active were more likely to transfer the intense strategies and therefore, they were expected to require less support or guidance. Students focusing on course requirements were not as effective self-regulators as they seemed and possibly required early guidance and support from teachers. Students with consistent light strategies or low effort needed proactive guidance and support. • We present methods for the longitudinal analysis of course strategies. • We identified 3 program-level trajectories: intense, light and interactive intense. • An intense trajectory was about deep learning strategies, stability and high grades. • A light interactive trajectory was fluctuating, less regulated and average grades. • The light trajectory was related to surface learning and lowest scores. [ABSTRACT FROM AUTHOR]

For service implementations to be widely adopted, it is necessary for the expectations of the key stakeholders to be considered. Failure to do so may lead to services reflecting ideological gaps, which will inadvertently create dissatisfaction among its users. Learning analytics research has begun to recognise the importance of understanding the student perspective towards the services that could be potentially offered; however, student engagement remains low. Furthermore, there has been no attempt to explore whether students can be segmented into different groups based on their expectations towards learning analytics services. In doing so, it allows for a greater understanding of what is and is not expected from learning analytics services within a sample of students. The current exploratory work addresses this limitation by using the three-step approach to latent class analysis to understand whether student expectations of learning analytics services can clearly be segmented, using self-report data obtained from a sample of students at an Open University in the Netherlands. The findings show that student expectations regarding ethical and privacy elements of a learning analytics service are consistent across all groups; however, those expectations of service features are quite variable. These results are discussed in relation to previous work on student stakeholder perspectives, policy development, and the European General Data Protection Regulation (GDPR). • Student expectations of learning analytics can be segmented based on service items. • Ethical and privacy expectations were relatively consistent across the sample. • Provides a discussion on how to integrate findings into institutional policies on learning analytics implementations. [ABSTRACT FROM AUTHOR]