19 research outputs found
Optimal Weighting for Exam Composition
A problem faced by many instructors is that of designing exams that
accurately assess the abilities of the students. Typically these exams are
prepared several days in advance, and generic question scores are used based on
rough approximation of the question difficulty and length. For example, for a
recent class taught by the author, there were 30 multiple choice questions
worth 3 points, 15 true/false with explanation questions worth 4 points, and 5
analytical exercises worth 10 points. We describe a novel framework where
algorithms from machine learning are used to modify the exam question weights
in order to optimize the exam scores, using the overall class grade as a proxy
for a student's true ability. We show that significant error reduction can be
obtained by our approach over standard weighting schemes, and we make several
new observations regarding the properties of the "good" and "bad" exam
questions that can have impact on the design of improved future evaluation
methods
Multi-Armed Bandits for Intelligent Tutoring Systems
We present an approach to Intelligent Tutoring Systems which adaptively
personalizes sequences of learning activities to maximize skills acquired by
students, taking into account the limited time and motivational resources. At a
given point in time, the system proposes to the students the activity which
makes them progress faster. We introduce two algorithms that rely on the
empirical estimation of the learning progress, RiARiT that uses information
about the difficulty of each exercise and ZPDES that uses much less knowledge
about the problem.
The system is based on the combination of three approaches. First, it
leverages recent models of intrinsically motivated learning by transposing them
to active teaching, relying on empirical estimation of learning progress
provided by specific activities to particular students. Second, it uses
state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the
exploration/exploitation challenge of this optimization process. Third, it
leverages expert knowledge to constrain and bootstrap initial exploration of
the MAB, while requiring only coarse guidance information of the expert and
allowing the system to deal with didactic gaps in its knowledge. The system is
evaluated in a scenario where 7-8 year old schoolchildren learn how to
decompose numbers while manipulating money. Systematic experiments are
presented with simulated students, followed by results of a user study across a
population of 400 school children
Personnalisation automatique des parcours d’apprentissage dans les Systèmes Tuteurs Intelligents
La recherche d’efficacité des systèmes tutoriels intelligents (STI) est un enjeu majeur. Nous présentons ici une méthode d’optimisation des parcoursd’apprentissage pour chaque apprenant. Nous cherchons à proposer à chaque instant à l’apprenant l’activité qui lui fait faire le plus de progrès dans son apprentissage. Nous introduisons deux algorithmes : RiARiT, qui nécessite des informations préalables sur les activités, et ZPDES, qui n’en a pas besoin
General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge
Knowledge Tracing is the de-facto standard for inferring student knowledge from performance data. Unfortunately, it does not allow modeling the feature-rich data that is now possible to collect in modern digital learning environments. Because of this, many ad hoc Knowledge Tracing variants have been proposed to model a specific feature of interest. For example, variants have studied the effect of students’ individual characteristics, the effect of help in a tutor, and subskills. These ad hoc models are successful for their own specific purpose, but are specified to only model a single specific feature. We present FAST (Feature Aware Student knowledge Tracing), an efficient, novel method that allows integrating general features into Knowledge Tracing. We demonstrate FAST’s flexibility with three examples of feature sets that are relevant to a wide audience. We use features in FAST to model (i) multiple subskill tracing, (ii) a temporal Item Response Model implementation, and (iii) expert knowledge. We present empirical results using data collected from an Intelligent Tutoring System. We report that using features can improve up to 25% in classification performance of the task of predicting student performance. Moreover, for fitting and inferencing, FAST can be 300 times faster than models created in BNT-SM, a toolkit that facilitates the creation of ad hoc Knowledge Tracing variants
Your model is predictive— but is it useful? Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation
Classification evaluation metrics are often used to evaluate adaptive tutoring systems— programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may lead to suboptimal decisions. We propose the Learner Effort-Outcomes Paradigm (Leopard), a new framework to evaluate adaptive tutoring. We introduce Teal and White, novel automatic metrics that apply Leopard and quantify the amount of effort required to achieve a learning outcome. Our experiments suggest that our metrics are a better alternative for evaluating adaptive tutoring
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
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