Novel framework for learning performance prediction using pattern identification and deep learning
| Date | 21 August 2024 |
| Pages | 111-133 |
| DOI | https://doi.org/10.1108/DTA-09-2023-0539 |
| Published date | 21 August 2024 |
| Author | Cheng-Hsiung Weng,Cheng-Kui Huang |
Novel framework for learning
performance prediction using
pattern identification and
deep learning
Cheng-Hsiung Weng
Department of Information Management,
National Changhua University of Education, Changhua, Taiwan, and
Cheng-Kui Huang
Department of Business Administration, National Chung Cheng University,
Minhsiung, Taiwan
Abstract
Purpose –Educational data mining (EDM) discovers significant patterns from educational data and thus can
help understand the relations between learners and their educational settings. However, most previous data
mining techniques focus on prediction of learning performance of learners without integrating learning
patterns identification techniques.
Design/methodology/approach –This study proposes a new framework for identifying learning patterns
and predicting learning performance. Two modules, the learning patterns identification module and the deep
learning prediction models (DNN), are integrated into this framework to identify the difference of learning
performance and predicting learning performance from profiles of students.
Findings –Experimental results from survey data indicate that the proposed identifying learning patterns
module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed
learning performance prediction module which adapts DNN also performs better than traditional machine
techniques in prediction performance metrics.
Originality/value –To our best knowledge, the framework is the only educational system in the literature for
identifying learning patterns and predicting learning performance.
Keywords Educational data mining, Learning difference, Deep learning, Deep neural network,
Frequent patterns mining, Machine learning
Paper type Research paper
1. Introduction
Educational data mining (EDM) is an emerging research area focused on discovering patterns
from educational data to help understand the relations betweenlearners and educational settings.
However, most educational data mining techniques focus on predicting learning performance
based on learners’profiles, rather than identifying their characteristics to evaluate their learning
performance. In particular, the characteristics of learners with low learning performance are
necessary to initiate early intervention for learners who need teaching assistance.
This study considers association-based classification patterns, which are used to identify
the associationsbetween cause and effect to establishstudents’learning performanceprofiles.
Furthermore, we propose a measure (OddsRatio) to determinevaluable patterns from each
Data
Technologies and
Applications
111
The authors would like to thank Dr Ruo-ping Han for Statistical Analysis. The research was supported
by the National Science and Technology Council of the Republic of China under the grants NSTC 112-
2410-H-018-044 and NSTC 112-2410-H-194-032-MY2.
Data availability: The datasets analyzed during the current study are available in the UCI Machine
Learning Repository, http://archive.ics.uci.edu/ml/.
Conflict of interest statement: There is no conflict of interest in this study.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 1 September 2023
Revised 12 December 2023
5 January 2024
20 April 2024
24 May 2024
Accepted 20 June 2024
Data Technologies and
Applications
Vol. 59 No. 1, 2025
pp. 111-133
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-09-2023-0539
cluster of instances. For example, (Pattern X
1
)→(Bad) means that students belonging to the
learning outcome(Bad) group have the characteristics: (Pattern X
1
). We also identify another
pattern: (Pattern X
2
)→(Good). Identifying the difference (ΔX) between patterns (X
1
and X
2
)
that leadsto different results is themain purpose of this study. Theknowledge in this example
reveals the association causes for the effect. Assume that we have two patterns, Pattern
X
1
5{(Paid 5no) →(Bad); support 50.92; count 5293; OddsRatio 51.08} and Pattern
X
2
5{(Higher 5yes,Paid 5no) →(Good); support 50.94; count 5289;OddsRatio 51.13};
therefore, the d ifference (ΔX) between two patterns (X
2
and X
1
) is {(Higher 5yes)}.
The above example indicates that difference pattern (ΔX), {(Higher 5yes)}, is a change
pattern for instances (students) with pattern X
1
5{(paid 5no) in clusterlearning performance
(Bad) who move to cluster learning performance (Good). The knowledge, pattern
{(Higher 5yes)}, in the above example reveals the association causesfor the effect, cluster
learning performance (Bad) moving to cluster learning performance (Good). However, no studies
in the education data mining field, to our knowledge, have addressed the important issue of
change patterns, difference (ΔX), identification in association-based classification patterns.
It is important for educational institutions to have approximate prior knowledge of
students to predict their performance in future academics. To address these problems, we
propose a framework for identifying learning patterns and predicting learning performance.
First, the learning patterns identification module is used for discovering difference patterns
that could identify the difference of learning performance among different clusters of
students. Second, deep learning prediction models (DNN) are employed for constructing
model to predict students’learning performance.
The rest of this paper is organized as follows. Section 2 reviews related work.
Methodology is given in Section 3. The experimental results are illustrated in Section 4.
Conclusions and future work are discussed in Section 5.
2. Related work
2.1 Frequent itemsets mining
Frequent pattern mining reveals intrinsic and important properties of datasets and is the
foundation of association rule mining. Mining frequent itemsets in association rule mining is
crucial (Agrawal et al., 1993). Most of the frequent itemset mining algorithms are improved or
derivative algorithms based on Apriori (Agrawal and Srikant, 1994) and FP-growth (Han
et al., 2000). More efficient methods for mining frequent itemsets have also been proposed,
such as H-mine (Pei et al., 2001) and Index-BitTableFI (Song et al., 2008). However, most of
these algorithms focus on improving the efficiency in frequent itemset mining processes,
rather than mining specific itemsets, such as specific later-marketed items. We provide
overview of the literature on frequent itemsets mining in Table 1.
2.2 Educational data mining
Data mining or knowledge discovery in databases (KDD) has been applied to some central
e-learning issues, such as the assessment of student’s learning performance and the
evaluation of learning materials and Web based courses. KDD can also be used to learn the
model for the learning process (H€
am€
al€
ainen et al., 2004) and student modeling (Tang and
McCalla, 2002), to evaluate and improve e-learning systems (Zaı€ane and Luo, 2001) and to
discover useful learning information from learning portfolios (Hwang et al., 2004).
The data mining techniques applied in these contexts enable course adaptation and
learning recommendations based on the students’learning behavior. These techniques also
enable feedback to teachers and students of e-learning courses and help identify typical
learning behavior (Castro et al., 2007;Baker and Yacef, 2009).
DTA
59,1
112
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