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Self‐regulated learning (SRL) is an essential skill to achieve one's learning goals. This is particularly true for online learning environments (OLEs) where the support system is often limited compared to a traditional classroom setting. Likewise, existing research has found that learners often struggle to adapt their behaviour to the self‐regulatory demands of OLEs. Even so, existing SRL analysis tools have limited utility for real‐time or individualised support of a learner's SRL strategy during a study session. Accordingly, we explore a reinforcement learning based approach to learning optimal SRL strategies for a specific learning task. Specifically, we utilise the sequences of SRL processes acted by 44 participants, and their assessment scores for a prescribed learning task, in a purpose‐built OLE to develop a long short‐term memory (LSTM) network based reward function. This is used to train a reinforcement learning agent to find the optimal sequence of SRL processes for the learning task. Our findings indicate that the developed agents were able to effectively select SRL processes so as to maximise a prescribed learning goal as measured by predicted assessment score and predicted knowledge gains. The contributions of this work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualised SRL focused scaffolding. Practitioner notesWhat is already known about this topic Learners often fail to adequately adapt their behavior to the self‐regulatory demands of e‐Learning environments.In order to promote effective Self‐regulated learning (SRL) capabilities, researchers and educators need tools that are able to analyze and diagnose a learner's SRL strategy use.Current methods for SRL analysis are more often descriptive as opposed to prescriptive and have limited utility for real‐time analysis or support of a learner's SRL behavior.What this paper adds This paper proposes the use of Reinforcement Learning for prescriptive analytics of SRL. We train a Reinforcement Learning agent on sequences of SRL processes acted by learners in order to learn the optimal SRL strategy for a given learning task.Implications for practice and/or policy Our work will facilitate the development of a tool which can detect sub‐optimal SRL strategy in real‐time and enable individualized SRL focused scaffolding.The implications of our work can aid in course design by predicting the self‐regulatory load imposed by a given task.The ability to model SRL strategies using Reinforcement Learning can be extended to simulate or test SRL theories. [ABSTRACT FROM AUTHOR]

Online discussion is one of the frequently used learning activities in an e‐learning process. Learners can exchange opinions and thoughts with their peers through online discussion, stimulate their different viewpoints on the discussion topics, and enhance the learning effectiveness of e‐learning. However, most learners mainly take an effort on posting discussion content but easily ignore reading and understanding the contents of peers' discussions, thus reducing the discussion effectiveness. The purpose of this study is to develop an Instant Perspective Comparison System with a Gamified Incentive Mechanism (IPCS‐GIM) to promote learners' discussion awareness and motivation, thereby effectively enhancing their discussion participation, motivation, and effectiveness. A true‐experimental design was conducted in this research to examine the research questions. A total of 38 university students in Taiwan were randomly assigned to the experimental group using the IPCS‐GIM to assist online discussion and the control group using an instant perspective comparison system without a gamified incentive mechanism (IPCS‐NGIM) to assist online discussion to examine their difference in discussion effectiveness, participation, and motivation on a social‐scientific issue (SSI). The analytical results show that compared to the IPCS‐NGIM, the IPCS‐GIM could not only significantly enhance learners' discussion effectiveness in overall discussion effectiveness, complexity, and multiple perspectives, but also significantly enhance learners' discussion participation and motivation. Practitioner notesWhat is already known about this topic An asynchronous online discussion that can allow learners to exchange opinions and thoughts with their peers, stimulate different viewpoints and enhance learning effectiveness is one of the frequently used learning activities in an e‐learning process. However, most learners mainly take an effort on posting discussion content but easily ignore reading and understanding the contents of peers' discussions, thus reducing the discussion effectiveness.An asynchronous online discussion may result in less enthusiastic discussions, learners may feel isolated due to the lack of immediate peer feedback, or they may not know what to contribute to a discussion topic.The lack of visual cues and immediate feedback is also one of the main factors affecting asynchronous online discussion effectiveness. In recent years, many studies have paid attention to promoting the discussion process and effectiveness by visualizing asynchronous online discussion content and interaction.What this paper adds This study uses gamified incentive mechanisms including a bonus point, title name, and leaderboard to develop a novel Instant Perspective Comparison System with a Gamified Incentive Mechanism (IPCS‐GIM) to promote learners' discussion effectiveness, participation, and motivation on social‐scientific issues (SSI) discussions.The result shows that compared to the Instant Perspective Comparison System without a Gamified Incentive Mechanism (IPCS‐NGIM), the IPCS‐GIM could not only significantly enhance learners' discussion effectiveness in overall discussion effectiveness, complexity, and multiple perspectives, but also significantly enhance learners' discussion participation and motivation.Implications for practice and/or policy Current research on the application of gamification design to asynchronous online discussions mainly focuses on enhancing learners' participation, engagement, and motivation, but there is a lack of research on the use of gamified incentive mechanisms to promote learners' reading of other peers' posts to achieve better quality discussions and enhance discussion effectiveness. This study fills the research gap.This study confirms that the proposed novel IPCS‐GIM can promote learners' discussion on reading other peers' posts, participation, and motivation to what extent due to its gamified incentive mechanisms, visualized discussion cues, and immediate feedback, thereby effectively enhancing learners' discussion effectiveness. This study broadens the horizon of the design of asynchronous online‐assisted discussion tools. [ABSTRACT FROM AUTHOR]

With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention has been paid to fairness in LA. Furthermore, the decision on which unfairness mitigation algorithm or metric to use in a particular context remains largely unknown. On this premise, we performed a comparative evaluation of some selected unfairness mitigation algorithms regarded in the fair ML community to have shown promising results. Using a 3‐year program dropout data from an Australian university, we comparatively evaluated how the unfairness mitigation algorithms contribute to ethical LA by testing for some hypotheses across fairness and performance metrics. Interestingly, our results show how data bias does not always necessarily result in predictive bias. Perhaps not surprisingly, our test for fairness‐utility tradeoff shows how ensuring fairness does not always lead to drop in utility. Indeed, our results show that ensuring fairness might lead to enhanced utility under specific circumstances. Our findings may to some extent, guide fairness algorithm and metric selection for a given context. Practitioner notesWhat is already known about this topic LA is increasingly being used to leverage actionable insights about students and drive student success.LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns.Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models.What this paper adds A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA.Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well.Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled.Implications for practice and/or policy Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness‐minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals.LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives.Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA. [ABSTRACT FROM AUTHOR]