The recent advances in educational technology enabled the development of solutions that collect and analyse data from learning scenarios to inform the decision-making processes. Research fields like Learning Analytics (LA) and Artificial Intelligence (AI) aim at supporting teaching and learning by using such solutions. However, their adoption in authentic settings is still limited, among other reasons, derived from ignoring the stakeholders' needs, a lack of pedagogical contextualisation, and a low trust in new technologies. Thus, the research fields of Human-Centered LA (HCLA) and Human-Centered AI (HCAI) recently emerged, aiming to understand the active involvement of stakeholders in the creation of such proposals. This paper presents a systematic literature review of 47 empirical research studies on the topic. The results show that more than two-thirds of the papers involve stakeholders in the design of the solutions, while fewer papers involved them during the ideation and prototyping, and the majority do not report any evaluation. Interestingly, while multiple techniques were used to collect data (mainly interviews, focus groups and workshops), few papers explicitly mentioned the adoption of existing HC design guidelines. Further evidence is needed to show the real impact of HCLA/HCAI approaches (e.g., in terms of user satisfaction and adoption).

Time management skills are crucial aspects of Self-Regulated Learning (SRL) that significantly influence students’ academic success. However, assessing these skills poses a considerable challenge. In contrast, online learning environments generate learning analytics that reflect students’ learning processes. By analyzing these data with suitable methods, researchers can gain insights into students’ time management strategies. One concrete manifestation of students’ time management is their assignment submission behavior. This study aims to understand students’ time management strategies through an analysis of these behaviors. To achieve this, the temporal patterns of students’ assignment submissions were examined to identify prevalent time management strategies. Additionally, the Epistemic Network Analysis (ENA) method was employed to conduct a comparative analysis of the strategies utilized by high-performing and low-performing students. Our findings indicate that students’ time management strategies can vary from one assignment to another. Therefore, assessing their time management strategies based on a single task may lead to incorrect classification. On the other hand, a statistically significant difference has been found in the assignment submission behaviors between high-performing and low-performing students.

Twitter is a very popular microblogging platform that has been actively used by scientific communities to exchange scientific information and to promote scholarly discussions. The present study aimed to leverage the tweet data to provide valuable insights into the development of the learning analytics field since its initial days. Descriptive analysis, geocoding analysis, and topic modeling were performed on over 1.6 million tweets related to learning analytics posted between 2010-2021. The descriptive analysis reveals an increasing popularity of the field on the Twittersphere in terms of number of users, twitter posts, and hashtags emergence. The topic modeling analysis uncovers new insights of the major topics in the field of learning analytics. Emergent themes in the field were identified, and the increasing (e.g., Artificial Intelligence) and decreasing (e.g., Education) trends were shared. Finally, the geocoding analysis indicates an increasing participation in the field from more diverse countries all around the world. Further findings are discussed in the paper.

Peer reviews offer many learning benefits. Understanding students’ engagement in them can help design effective practices. Although learning analytics can be effective in generating such insights, its application in peer reviews is scarce. Theory can provide the necessary foundations to inform the design of learning analytics research and the interpretation of its results. In this paper, we followed a theory-based learning analytics approach to identifying students’ engagement patterns in a peer review activity facilitated via a web-based tool called Synergy. Process mining was applied on temporal learning data, traced by Synergy. The theory about peer review helped determine relevant data points and guided the top-down approach employed for their analysis: moving from the global phases to regulation of learning, and then to micro-level actions. The results suggest that theory and learning analytics should mutually relate with each other. Mainly, theory played a critical role in identifying a priori engagement patterns, which provided an informed perspective when interpreting the results. In return, the results of the learning analytics offered critical insights about student behavior that was not expected by the theory (i.e., low levels of co-regulation). The findings provided important implications for refining the grounding theory and its operationalization in Synergy.

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples.

With the aim of supporting instructional designers in setting up collaborative learning activities in MOOCs, this paper derives prediction models for student participation in group discussions. The salient feature of these models is that they are built using only data prior to the learning activity, and can thus provide actionable predictions, as opposed to post-hoc approaches common in the MOOC literature. Some learning design scenarios that make use of this actionable information are illustrated.

Collaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students’ engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course.

Building public Internet access points (PIAPs) is a significant contribution of governments towards achieving an information society. While many developing countries are investing great amounts to establish PIAPs today, people may not use PIAPs effectively. Yet, the successful implementation of PIAPs is the result of citizens' acceptance to use this opportunity. Hence, based on the Diffusion of Innovation Model, this study aims to detect the determinants of behavioural intention to use PIAPs. Essential data were collected from 3477 users of BELNET PIAPs established by the municipality of Istanbul in Turkey. We found that observability, compatibility and relative advantage all positively impact the intention to use PIAPs. The results reveal academic and practical implications for the future development and implementation of PIAPs.