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  1. ; ; ( , Proceedings of the 16th International Conference on Educational Data Mining)
    The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two decades, the EDM community has taken consistent steps to protect learner privacy within our research, whilst pursuing goals that will benefit their learning. However, recent privacy legislation may cause our practices to need to change. The right to be forgotten states that users have the right to request that all their data (including deidentified data generated by them) be removed. In this paper, we discuss the potential challenges of this legislation for EDM research, including impacts on Open Science practices, Data Modeling, and Data sharing. We also consider changes to EDM best practices that may aid compliance with this new legislation. 
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    Free, publicly-accessible full text available July 5, 2024
  2. ; ; ( , L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale)
    Massive Open Online Courses (MOOCs) have increased the accessibility of quality educational content to a broader audience across a global network. They provide access for students to material that would be difficult to obtain locally, and an abundance of data for educational researchers. Despite the international reach of MOOCs, however, the majority of MOOC research does not account for demographic differences relating to the learners' country of origin or cultural background, which have been shown to have implications on the robustness of predictive models and interventions. This paper presents an exploration into the role of nation-level metrics of culture, happiness, wealth, and size on the generalizability of completion prediction models across countries. The findings indicate that various dimensions of culture are predictive of cross-country model generalizability. Specifically, learners from indulgent, collectivist, uncertainty-accepting, or short-term oriented, countries produce more generalizable predictive models of learner completion. 
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    Free, publicly-accessible full text available July 20, 2024
  3. ; ; ; ; ( , Proceedings of the ACM on Human-Computer Interaction)
    Eye movements provide a window into cognitive processes, but much of the research harnessing this data has been confined to the laboratory. We address whether eye gaze can be passively, reliably, and privately recorded in real-world environments across extended timeframes using commercial-off-the-shelf (COTS) sensors. We recorded eye gaze data from a COTS tracker embedded in participants (N=20) work environments at pseudorandom intervals across a two-week period. We found that valid samples were recorded approximately 30% of the time despite calibrating the eye tracker only once and without placing any other restrictions on participants. The number of valid samples decreased over days with the degree of decrease dependent on contextual variables (i.e., frequency of video conferencing) and individual difference attributes (e.g., sleep quality and multitasking ability). Participants reported that sensors did not change or impact their work. Our findings suggest the potential for the collection of eye-gaze in authentic environments. 
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    Full Text Available 
  4. ; ( , ACM International Conference on Multimodal Interaction)
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  5. ; ; ; ( , Proceedings of Learning with MOOCs 2022)
    Accepted Manuscript Full Text Available
  6. ; ; ; ( , British journal of educational technology)
    Full Text Available 
  7. ; ; ; ; ( , Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society)
    Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms of the design of the student interface, assessment models, and data collection log schemas. Can gaming detectors still be trusted, a decade or more after they are developed? In this research, we evaluate the robustness/degradation of gaming detectors when trained on old data logs and evaluated on current data logs. We demonstrate that some machine learning models developed using past data are still able to predict gaming behavior from student data collected 16 years later, but that there is considerable variance in how well different algorithms perform over time. We demonstrate that a classic decision tree algorithm maintained its performance while more contemporary algorithms struggled to transfer to new data, even though they exhibited better performance on both new and old data alone. Examining the feature importances provides some explanation for the differences in performance between models, and offers some insight into how we might safeguard against detector rot over time. 
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    Full Text Available 
  8. ; ; ; ( , Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems)
    null (Ed.)
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  9. ; ; ; ; ; ( , Journal of Computer Assisted Learning)
    Abstract Background

    Providing adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks.

     Objectives

    This paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning‐by‐teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs.

     Methods

    We detect key cognitive/metacognitiveinflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context‐specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre‐to‐post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain.

     Results and Conclusions

    Adaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students.

     Implications

    This paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.

     
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  10. ; ; ; ; ; ; ; ; ( , American Educational Research Journal)

    Compared with admissions test scores, why are high school grades better at predicting college graduation? We argue that success in college requires not only cognitive ability but also self-regulatory competencies that are better indexed by high school grades. In a national sample of 47,303 students who applied to college for the 2009/2010 academic year, Study 1 affirmed that high school grades out-predicted test scores for 4-year college graduation. In a convenience sample of 1,622 high school seniors in the Class of 2013, Study 2 revealed that the incremental predictive validity of high school grades for college graduation was explained by composite measures of self-regulation, whereas the incremental predictive validity of test scores was explained by composite measures of cognitive ability.

     
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