Zacharoula Papamitsiou

In the era of digitalization of learning and teaching processes, Educational Data Literacy (EDL) is highly valued and is becoming essential. EDL is conceptualized as the ability to collect, manage, analyse, comprehend, interpret, and act upon educational data in an ethical, meaningful, and critical manner. The professionals in the field of digitally supported education, i.e., Instructional Designers (IDs) and e-Tutors (eTUTs) of online and blended courses, need to be ready to inform their decisions with educational data, and face the upcoming data-related challenges; they need to update and enhance their profiles with relevant competences. This paper proposes a framework for EDL competence profiles of IDs/eTUTs and evaluates the proposal with the participation of worldwide professionals (N = 210) with experience in digitally supported education. The evaluation aims at validating the proposal and assesses (a) the current EDL-readiness of IDs/eTUTs; and (b) the extent to which the framework captures and describes the essential EDL competences. The findings indicate that professionals are not EDL-competent yet, but the proposed dimensions and related competences are offering a solid approach to support EDL development.

Investigating and explaining the patterns of learners' engagement in adaptive learning conditions is a core issue towards improving the quality of personalized learning services. This study collects learner data from multiple sources during an adaptive learning activity, and employs a fuzzy set qualitative comparative analysis (fsQCA) approach to shed light to learners' engagement patterns, with respect to their learning performance. Specifically, this study measures and codes learners' engagement by fusing and compiling clickstreams (e.g., response time), physiological data (e.g., eye-tracking, EEG, electrodermal activity) and survey data (e.g., goal-orientation) to determine what configurations of those data explain when learners can attain high or medium/low learning performance. For the evaluation of the approach, an empirical study with 32 undergraduates was conducted. The analysis revealed six configurations that explain learners' high performance and three that explain learners' medium/low performance, driven by engagement measures coming from the multimodal data. Since fsQCA explains the outcome of interest itself, rather than its variance, these findings advance our understanding on the combined effect of the multiple indicators of engagement on learners' performance. Limitations and potential implications of the findings are also discussed.

This longitudinal study investigates the differences in learners' effortful behaviour over time due to receiving metacognitive help—in the form of on‐demand task‐related visual analytics. Specifically, learners' interactions (N = 67) with the tasks were tracked during four self‐assessment activities, conducted at four discrete points in time, over a period of 8 weeks. The considered and coded time points were: (a) prior to providing the metacognitive help; (b) while the task‐related visual analytics were available (treatment); (c) after the removal of the treatment; and (d) while the option to receive metacognitive help was available again. To measure learners' effortful behaviour across the self‐assessment activities, this study utilized learners' response‐times to correctly/wrongly complete the tasks and on‐task effort expenditure. The panel data analysis shown that the usage of metacognitive help caused statistically significant changes in learners' effortful behaviour, mostly in the third and fourth phase. Statistically significant changes were detected also in the usage of metacognitive help. These results provide empirical evidence on the benefits of task‐related visual analytics to support learners' on‐task engagement, and suggest relevant cues on how metacognitive help could be designed and prompted by focusing on the “task”, instead of the “self”.

This study aims to identify the conceptual structure and the thematic progress in e-Business, e-Services and e-Society and to elaborate on backbone/emerging topics in the field from 2001 to 2019. To address this objective, this paper employs hierarchical clustering, strategic diagrams and network analysis to construct the intellectual map of the I3E community and to visualize the thematic landscape in this field, using co-word analysis. Overall, a total of 835 papers from the proceedings of the e-Business, e-Services and e-Society (I3E) conference, and the respective 2574 author-assigned keywords, were included in the analyses. The results indicate that the community has significantly focused in areas like technology adoption models, Social Media, e-Government and business models; sentiment analysis and m-payments are peripheral themes, yet topics like cloud computing and Open Data are emerging. The analysis highlights the shift of the research interest throughout the past decades, and the rise of new topics, comprising evidence that the field is expanding and evolving. Limitations of the approach and future work plans conclude the paper.

Practising self‐regulated learning (SRL) has been proposed to develop learning autonomy. However, there is lack of empirical evidence on how SRL strategies affect autonomous learning capacity. This study attempts to bridge that gap by utilizing the learners’ trace data for measuring the learners’ autonomous interactions, and investigates the effects of four SRL strategies on learners’ autonomous choices. The goal is to explain how the employed SRL strategies impact autonomous control (in terms of frequencies of self‐enforced decisions, as well as time‐spent on decision making). The results from an exploratory study with undergraduate learners ( N = 113) shown that goal‐setting and time‐management have strong positive effects on autonomous control, effort‐regulation moderately positively affects learners’ autonomy, while help‐seeking has a strong negative effect. These findings provide empirical evidence and contribute to clarifying the role of each one of the SRL strategies in the development of autonomous learning capacity, from a learning analytics perspective. Limitations and potential implications for research and practice are also discussed.

Recommending educational resources to groups of students is a common task in collaborative learning contexts. However, differences in within-group motivational factors, might lead to conflicts in students' intention to use the resources. Previous methods fail to achieve high goodness of recommendation for the majority of students in heterogeneous groups. This study demonstrates a game-theoretic solution for recommending educational resources to homogeneous and heterogeneous groups. The group members are the players, the resources comprise the set of possible actions, and selecting those items that will maximize all students' motivation in the collaborative activity is a problem of finding the Nash Equilibrium. In case the Nash Equilibrium is Pareto efficient, none of the players can get more payoff (motivation) without decreasing the payoff of any other player, indicating an optimal benefit for the group as a whole. The suggested approach was empirically evaluated in a controlled experiment with a real dataset. The relevance of each delivered item to its corresponding students was explored both from the perspective of the group and its the individual students. The accuracy of the predicted group/individual motivation, the goodness of the ranked list of recommendations, and the problem-solving performance for the treatment group were significantly higher compared to the control groups. Limitations of the approach, as well as future work plans conclude the paper.