22 research outputs found
Towards Interpretable Deep Learning Models for Knowledge Tracing
As an important technique for modeling the knowledge states of learners, the
traditional knowledge tracing (KT) models have been widely used to support
intelligent tutoring systems and MOOC platforms. Driven by the fast
advancements of deep learning techniques, deep neural network has been recently
adopted to design new KT models for achieving better prediction performance.
However, the lack of interpretability of these models has painfully impeded
their practical applications, as their outputs and working mechanisms suffer
from the intransparent decision process and complex inner structures. We thus
propose to adopt the post-hoc method to tackle the interpretability issue for
deep learning based knowledge tracing (DLKT) models. Specifically, we focus on
applying the layer-wise relevance propagation (LRP) method to interpret
RNN-based DLKT model by backpropagating the relevance from the model's output
layer to its input layer. The experiment results show the feasibility using the
LRP method for interpreting the DLKT model's predictions, and partially
validate the computed relevance scores from both question level and concept
level. We believe it can be a solid step towards fully interpreting the DLKT
models and promote their practical applications in the education domain
Determinants of Efficacy of Studying in the Republic Croatia - Comparing Neural Networks and Decision Trees: Research Framework Proposition
Rapid technological development and progress lead to the need for better and more efficient education which should prepare the applicant for increasingly flexible labour market. The goal of this research is to create models for prediction of student’s efficacy, compare them, find the key factors that contribute to more efficient studying in the Republic of Croatia, and finally determine how efficient studying is related to first employment. Models will be based on students’ data and hypothesis will be tested using multivariate statistical methods (multiple regressions, Cronbach’s alpha), decision trees and neural networks. Data will be collected by structured questionnaire and will consist of demographic and economic data, information about previous education, attitudes towards learning, and goals after completing studies and information about the first employment. Students’ efficacy will be measured by grade point average in college. This research will try to increase our understanding of how different factors influence students’ performance and how students’ efficacy affects the speed and conditions of finding the first employment.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p
Overcoming transactional distance when conducting online classes on programming for business students: a COVID-19 experience
Studies have shown that transactional distance negatively impacts student learning. In the context of learning, distance pertains to the geographic, pedagogical, and psychological gap between instructors and students. This perception of distance is magnified in online learning because instructors and students do not meet face to face. The gaps involve not only the geographic aspect. Another gap is pedagogical, which depends on the online course\u27s design and structure flexibility and how these align with the students\u27 level of autonomy. Still, another gap is psychological, which relates to how students perceive how much the teacher is accessible or disengaged (level of dialogue) and with students\u27 academic self-efficacy assessments. This paper describes how we could reduce the transactional distance between instructor and students by deliberately designing and conducting mostly asynchronous classes on programming for business students but with the right blend of non-lecture synchronous activities during tight lockdown due to COVID-19. We explain what used to work well before the pandemic where classes were onsite and face-to-face and what mechanisms we used to overcome the lockdown-related gaps. The course was held during Intersession and only had less than six weeks. Based on students\u27 grades and general sentiments, the results were in line with expected learning outcomes, and miscellaneous feedback and comments from students were positive
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model
Knowledge tracing consists in predicting the performance of some students on
new questions given their performance on previous questions, and can be a prior
step to optimizing assessment and learning. Deep knowledge tracing (DKT) is a
competitive model for knowledge tracing relying on recurrent neural networks,
even if some simpler models may match its performance. However, little is known
about why DKT works so well. In this paper, we frame deep knowledge tracing as
a encoderdecoder architecture. This viewpoint not only allows us to propose
better models in terms of performance, simplicity or expressivity but also
opens up promising avenues for future research directions. In particular, we
show on several small and large datasets that a simpler decoder, with possibly
fewer parameters than the one used by DKT, can predict student performance
better.Comment: ICCE 2023 - The 31st International Conference on Computers in
Education, Asia-Pacific Society for Computers in Education, Dec 2023, Matsue,
Shimane, Franc
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),
201
Recommender system for predicting student performance
AbstractRecommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results