An Introduction to Bayesian Knowledge Tracing with pyBKT

Bulut, Okan; Shin, Jinnie; Yildirim-Erbasli, Seyma Nur; Gorgun, Guher; Pardos, Zachary A.

doi:10.3390/psych5030050

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…AIEd has been under active development for several decades now [31] and has made significant advances on different fronts, especially in areas such as Intelligent Tutoring Systems (ITSs) [32,33], educational recommendations [21], MOOC management [28,34] and taxonomy/prerequisite detection [35], among others. Recent work has also shown that AI could be used to accumulate learning resources at scale [20], as well as enrich these to break language barriers by creating cross-lingual translations/transcriptions [36], domain-/language-agnostic topic annotations [37], and visual and interactive content summaries that support the learner, providing synthetic video materials that are as effective as learning from human teachers' video recordings [38].…”

Section: The Promisementioning

confidence: 99%

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

Bulathwela,

Pérez-Ortiz,

Holloway

et al. 2024

Sustainability

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Artificial Intelligence (AI) in Education claims to have the potential for building personalised curricula, as well as bringing opportunities for democratising education and creating a renaissance of new ways of teaching and learning. Millions of students are starting to benefit from the use of these technologies, but millions more around the world are not, due to the digital divide and deep pre-existing social and educational inequalities. If this trend continues, the first large-scale delivery of AI in Education could lead to greater educational inequality, along with a global misallocation of educational resources motivated by the current techno-solutionist narrative, which proposes technological solutions as a quick and flawless way to solve complex real-world problems. This work focuses on posing questions about the future of AI in Education, intending to initiate the pressing conversation that could set the right foundations (e.g., inclusion and diversity) for a new generation of education that is permeated with AI technology. The main goal of our opinion piece is to conceptualise a sustainable, large-scale and inclusive AI for the education ecosystem that facilitates equitable, high-quality lifelong learning opportunities for all. The contribution starts by synthesising how AI might change how we learn and teach, focusing on the case of personalised learning companions and assistive technology for disability. Then, we move on to discuss some socio-technical features that will be crucial to avoiding the perils of these AI systems worldwide (and perhaps ensuring their success by leveraging more inclusive education). This work also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We emphasise the need for collectively designing human-centred, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as supporting new emerging pedagogies. Finally, we ask what it would take for this educational revolution to provide egalitarian and empowering access to education that transcends any political, cultural, language, geographical and learning-ability barriers, so that educational systems can be responsive to all learners’ needs.

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Section: The Promisementioning

confidence: 99%

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

Bulathwela,

Pérez-Ortiz,

Holloway

et al. 2024

Sustainability

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“…Across all analyses, we fit all Bayesian Knowledge Tracing models using the Python implementation of BKT in the package "pyBKT" (Badrinath et al, 2021;Bulut et al, 2023).…”

Section: Fitting Procedure Metrics and Analytic Approachmentioning

confidence: 99%

Capturing Session-to-Session Dynamics of Learning and Forgetting: Testing the Limits of Knowledge Tracing Models

Schuetze,

Yan,

Carvalho

2024

Preprint

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The development of intelligent tutoring systems and other educational technology necessitated the implementation and development of computational models of student learning. At the foundation of these models is the assumption that they accurately implement and track human cognitive processes. However, the extent to which this assumption is correct has not been fully tested. In the current paper, we use data from a large-scale longitudinal lab study to investigate the match between the processes instantiated in the models and human memory and learning processes. When fitted to the entire dataset retrospectively, the selected models of student learning (Bayesian Knowledge Tracing and Additive Factors Model) capture the qualitative trends of learning across sessions and relatively acceptable fit metrics. However, when the models are used to predict future behavior, as is often the goal in applied contexts, the picture changes considerably. We show that two of the most popular types of student learning models fail to account for basic cognitive principles—the spacing effect, and patterns of forgetting and learning across sessions. In fact, in some instances, having a poor model of human learning and memory may be worse than having no model of human learning and memory at all. At the same time, cognitive approaches can and should greatly benefit from computational modeling approaches; computational modeling offers opportunities for furthering both theory and practical impact.

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“…The article by Bulut, Shin, Yildirim-Erbasli, Gorgun, and Pardos [18], titled "An introduction to Bayesian knowledge tracing with pyBKT", provides a tutorial in Bayesian knowledge tracing (BKT). Also, it is a tutorial for estimating BKT models in the pyBKT library in the Python software.…”

mentioning

confidence: 99%

Editorial for the Special Issue “Computational Aspects and Software in Psychometrics II”

Robitzsch

2023

Psych

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An Introduction to Bayesian Knowledge Tracing with pyBKT

Cited by 6 publications

References 32 publications

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

Capturing Session-to-Session Dynamics of Learning and Forgetting: Testing the Limits of Knowledge Tracing Models

Editorial for the Special Issue “Computational Aspects and Software in Psychometrics II”

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