8,490 research outputs found
Accommodating the difference in studentsâ prior knowledge of cell growth kinetics
This paper describes the development and benefits of an adaptive digital module on cell growth to tackle the problem of educating a heterogeneous group of students at the beginning of an undergraduate course on process engineering. Aim of the digital module is to provide students with the minimal level of knowledge on cell growth kinetics they need to comprehend the content knowledge of the subsequent lectures and pass the exam. The module was organised to offer the subject matter in a differentiated manner, so that students could follow different learning paths. Two student groups were investigated, one consisting of students who had received their prior education abroad and one of students that had not. Exam scores, questionnaires, and logged user data of the two student groups were analysed to discover whether the digital module had the intended effect. The results indicate that students did indeed follow different learning paths. Also, the differences in exam scores between the two student groups that was present before the introduction of the digital module was found to have decreased afterwards. In general, students appreciated the use of the material regardless of their prior education. We therefore conclude that the use of adaptive digital learning material is a possible way to solve the problem of differences in prior education of students entering a course
An intelligent tutoring system for the investigation of high performance skill acquisition
The issue of training high performance skills is of increasing concern. These skills include tasks such as driving a car, playing the piano, and flying an aircraft. Traditionally, the training of high performance skills has been accomplished through the use of expensive, high-fidelity, 3-D simulators, and/or on-the-job training using the actual equipment. Such an approach to training is quite expensive. The design, implementation, and deployment of an intelligent tutoring system developed for the purpose of studying the effectiveness of skill acquisition using lower-cost, lower-physical-fidelity, 2-D simulation. Preliminary experimental results are quite encouraging, indicating that intelligent tutoring systems are a cost-effective means of training high performance skills
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
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
Using dialogue to learn math in the LeActiveMath project
We describe a tutorial dialogue system under development that assists students in learning how to differentiate equations. The system uses deep natural language understanding and generation to both interpret students â utterances and automatically generate a response that is both mathematically correct and adapted pedagogically and linguistically to the local dialogue context. A domain reasoner provides the necessary knowledge about how students should approach math problems as well as their (in)correctness, while a dialogue manager directs pedagogical strategies and keeps track of what needs to be done to keep the dialogue moving along.
The influence of student characteristics on the use of adaptive e-learning material
Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive elearning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning materia
Towards Goal-oriented Intelligent Tutoring Systems in Online Education
Interactive Intelligent Tutoring Systems (ITSs) enhance traditional ITSs by
promoting effective learning through interactions and problem resolution in
online education. Yet, proactive engagement, prioritizing resource optimization
with planning and assessment capabilities, is often overlooked in current ITS
designs. In this work, we investigate a new task, named Goal-oriented
Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery
of a designated concept by strategically planning a customized sequence of
exercises and assessment. To address the problem of goal-oriented policy
learning in GITS, we propose a novel graph-based reinforcement learning
framework, named Planning-Assessment-Interaction (PAI). Specifically, we first
leverage cognitive structure information to improve state representation
learning and action selection for planning the next action, which can be either
to tutor an exercise or to assess the target concept. Further, we use a
dynamically updated cognitive diagnosis model to simulate student responses to
exercises and concepts. Three benchmark datasets across different subjects are
constructed for enabling offline academic research on GITS. Experimental
results demonstrate the effectiveness and efficiency of PAI and extensive
analyses of various types of students are conducted to showcase the challenges
in this task
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