21 research outputs found
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing
either model knowledge state for each predefined concept separately or fail to
pinpoint exactly which concepts a student is good at or unfamiliar with. To
solve these problems, this work introduces a new model called Dynamic Key-Value
Memory Networks (DKVMN) that can exploit the relationships between underlying
concepts and directly output a student's mastery level of each concept. Unlike
standard memory-augmented neural networks that facilitate a single memory
matrix or two static memory matrices, our model has one static matrix called
key, which stores the knowledge concepts and the other dynamic matrix called
value, which stores and updates the mastery levels of corresponding concepts.
Experiments show that our model consistently outperforms the state-of-the-art
model in a range of KT datasets. Moreover, the DKVMN model can automatically
discover underlying concepts of exercises typically performed by human
annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW),
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Towards integration of adaptive educational systems: mapping domain models to ontologies
With the growth of adaptive educational systems available for
students, semantic integration of user modeling information from these systems
is emerging into an important practical task. Ontologies can serve as the major
representational framework for such integration. However, not all adaptive
systems rely on ontologies for representing domain knowledge. In this paper,
we report an experiment on integration of domain models of two different
adaptive systems
Predicting learners' effortful behaviour in adaptive assessment using multimodal data
Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. However, effort is not directly observable. Multimodal data can provide additional insights into the learning processes and may allow for effort estimation. This paper presents an approach for the classification of effort in an adaptive assessment context. Specifically, the behaviour of 32 students was captured during an adaptive self-assessment activity, using logs and physiological data (i.e., eye-tracking, EEG, wristband and facial expressions). We applied k-means to the multimodal data to cluster students' behavioural patterns. Next, we predicted students' effort to complete the upcoming task, based on the discovered behavioural patterns using a combination of Hidden Markov Models (HMMs) and the Viterbi algorithm. We also compared the results with other state-of-the-art classification algorithms (SVM, Random Forest). Our findings provide evidence that HMMs can encode the relationship between effort and behaviour (captured by the multimodal data) in a more efficient way than the other methods. Foremost, a practical implication of the approach is that the derived HMMs also pinpoint the moments to provide preventive/prescriptive feedback to the learners in real-time, by building-upon the relationship between behavioural patterns and the effort the learners are putting in