Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

Fratamico, Lauren; Conati, Cristina; Kardan, Samad; Roll, Ido

doi:10.1007/s40593-016-0131-y

Cited by 24 publications

(19 citation statements)

References 32 publications

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“…Recently, by analysing the online behaviour of students while working with a simulation on electrical circuits and comparing low prior knowledge students who learned a lot (LH students) with low prior knowledge students who did not gain much knowledge (LL students), Perez et al () found that the LH students inserted pauses in their learning process much more than the LL students, obviously to reflect upon experiments they had done (and to plan for experiments to come). A similar effect was reported by Fratamico et al (); Bumbacher, Salehi, Wierzchula, and Blikstein () also reported that inserting a delay between experiments was related to being successful on a domain knowledge test after an inquiry session.…”

Section: The Next Steps In Technology‐based Guidance Of the Inquiry Psupporting

confidence: 82%

“…For example, getting a hold on students' inquiry skills (e.g., their capacity to design sound experiments) and inferring these from their interaction behaviour is difficult because there is not a sequential learning path and effective behaviours can be very different (Fratamico, Conati, Kardan, & Roll, ). However, the situation is improving, and several systems that give individualized and adaptive guidance have now been developed.…”

Section: The Next Steps In Technology‐based Guidance Of the Inquiry Pmentioning

confidence: 99%

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Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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What is the best approach to educating students is, evidently, the pivotal question in educational research. In the general debate on this question, clear positions are often taken―for example, whether teacher‐led instruction or more student‐directed approaches should be followed. In the current narrative review, this central question and the different stances taken are explored for a specific field of learning, STEM (science, technology, engineering, and mathematics) domains. The current article starts with an argument as to why the traditional, more directive teacher‐led educational approach to STEM learning may not always lead to deep conceptual knowledge and proposes exploring more engaged forms of learning. More specifically, a particular arrangement for engaged learning, inquiry learning, is taken as an example in this exploration. It is argued that the effects of educational methods are influenced by a multitude of (interacting) factors related to student and teacher characteristics, context, domain, frequency of use, timing, and probably more. Therefore, this article tries to outline a more nuanced and balanced stance towards choosing educational approaches. A picture is presented to show that adopting and combining different approaches, at both the lesson and the curricular levels, may be necessary to reach optimal levels of learning. Existing and upcoming technologies may play a decisive role in realizing this more complex, but also more realistic view of learning in STEM.

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Section: The Next Steps In Technology‐based Guidance Of the Inquiry Psupporting

confidence: 82%

Section: The Next Steps In Technology‐based Guidance Of the Inquiry Pmentioning

confidence: 99%

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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“…AI is also very effective in predicting student cognitive needs, results, mental states and skills and subsequently recommending the right course of action. For example, ITS systems with AI enhancements are applicable in modelling student emotions [77], efficacy [78], ability to perform scientific enquiry within a virtual environment [79] and then generate recommendations automatically [80].…”

Section: Intelligent Tutoring Systems (Its)mentioning

confidence: 99%

Data-Driven Artificial Intelligence in Education: A Comprehensive Review

Ahmad¹,

Qadir²,

Al-Fuqaha³

et al. 2020

Preprint

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Motivated by the importance of education in an individual's and a society's development, researchers have been exploring the use of Artificial Intelligence (AI) in the domain and have come up with myriad potential applications. This paper pays particular attention to this issue by highlighting the future scope and market opportunities for AI in education, the existing tools and applications deployed in several applications of AI in education, research trends, current limitations and pitfalls of AI in education. In particular, the paper reviews the various applications of AI in education including student grading and evaluations, students' retention and drop out prediction, sentiment analysis, intelligent tutoring, classrooms' monitoring and recommendation systems. The paper also provides a detailed bibliometric analysis to highlight the research trends in the domain over six years (2014--2019). For this study, we analyze research publications in various related sub-domains such as learning analytics, educational data mining (EDM), and big data in education. The paper analyzes educational applications from different perspectives. On the one hand, it provides a detailed description of the tools and platforms developed as the outcome of the research work achieved in these applications. On the other side, it identifies the potential challenges, current limitations and hints for further improvement. We also provide important insights into the use and pitfalls of AI in education. We believe such rigorous analysis will provide a baseline for future research in the domain.

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“…While the students who received adaptive support did not have improved task performance, they learned significantly more than those who did not receive the adaptive support. Fratamico et al in (Fratamico et al, 2017) applied Kardan and Conati's 2015 framework to an electronic circuits simulator and found that it successfully classified students into groups of high and low learners. Mostafavi, et al similarly applied machine learning to form datadriven proficiency profiles used in problem selection (Mostafavi et al, 2015), and showed that it reduced the time taken in a previous version of the Deep Thought logic tutor.…”

Section: Assistance Dilemmamentioning

confidence: 99%

“…Several studies have explored ways to determine the timing of assistance, as well as scaffolding in open-ended domains to improve learning (Fossati et al, 2015;Ueno and Miyazawa, 2017;Borek et al, 2009;Kardan and Conati, 2015), and prevent student failure in exams (Merceron and Yacef, 2005). While some researchers have explored the generalizability of such approaches (Fratamico et al, 2017;Bunt and Conati, 2003), determining when to provide assistance is still a challenging task for most open-ended domains, particularly because of differences in domains and learners (Klahr, 2009;McLaren et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help

Maniktala,

Cody,

Isvik

et al. 2020

Preprint

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Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene. Such a task is particularly challenging for open-ended domains, even those that are well-structured with defined principles and goals. In this paper, we present a set of data-driven methods to classify, predict, and prevent unproductive problem-solving steps in the well-structured open-ended domain of logic. This approach leverages and extends the Hint Factory, a set of methods that leverages prior student solution attempts to build data-driven intelligent tutors. We present a HelpNeed classification, that uses prior student data to determine when students are likely to be unproductive and need help learning optimal problem-solving strategies. We present a controlled study to determine the impact of an Adaptive pedagogical policy that provides proactive hints at the start of each step based on the outcomes of our HelpNeed predictor: productive vs. unproductive. Our results show that the students in the Adaptive condition exhibited better training behaviors, with lower help avoidance, and higher help appropriateness (a higher chance of receiving help when it was likely to be needed), as measured using the HelpNeed classifier, when compared to the Control. Furthermore, the results show that the students who received Adaptive hints based on HelpNeed predictions during training significantly outperform their Control peers on the posttest, with the former producing shorter, more optimal solutions in less time. We conclude with suggestions on how these HelpNeed methods could be applied in other well-structured open-ended domains.

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Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

Cited by 24 publications

References 32 publications

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Data-Driven Artificial Intelligence in Education: A Comprehensive Review

Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help

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