Classification-Based Approach to Concept Map Generation in Adaptive Learning

Huang, Xiaopeng; Yang, Kyeong Ho; Lawrence, Victor B.

doi:10.1109/icalt.2015.149

Cited by 7 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou et al [208] provides a new insight into the non-convergence issue of Adam, an adaptive learning rate method, proposing a novel method called AdaShift that decorrelates gradient and second-moment terms to address the non-convergence problem while maintaining competitive performance. Huang, Yang and Lawrence [74] proposes a classification-based approach to improve the accuracy and reduce the computational complexity of data mining-based concept map generation in adaptive learning systems. Hämäläinen, Kumpulainen and Mozgovoy [77] discusses the clustering of student data, which is a central task in educational data mining and the design of intelligent learning tools.…”

Section: A Cluster Of General Concepts Of Adaptive Learning In E-lear...mentioning

confidence: 99%

Adaptive learning: a cluster-based literature review (2011-2022)

Fadieieva

2023

Educ. Technol. Q.

View full text Add to dashboard Cite

Adaptive learning is a personalized instruction system that adjusts to the needs, preferences, and progress of learners. This paper reviews the current and future developments of adaptive learning in higher education, especially in relation to the digital education strategy of the European Union. It also uses a cluster analysis framework to explore the main themes and their relationships in the academic literature on adaptive learning. The paper highlights the potential of emerging technologies such as AI, eye-tracking, and physiological measurements to improve the personalization and effectiveness of adaptive learning systems. It presents various methods, algorithms, and prototypes that incorporate learning styles into adaptive learning. It also stresses the importance of continuous professional development in e-learning, media literacy, computer security, and andragogy for teachers who use adaptive learning systems. The paper concludes that adaptive learning can promote creativity, innovation, and lifelong learning in Ukrainian higher education, but it also acknowledges the challenges and suggests further research to assess its impact.

show abstract

Section: A Cluster Of General Concepts Of Adaptive Learning In E-lear...mentioning

confidence: 99%

Adaptive learning: a cluster-based literature review (2011-2022)

Fadieieva

2023

Educ. Technol. Q.

View full text Add to dashboard Cite

show abstract

“…This method could update the concept map in time following the learner's answer records, which could match the requirements of the adaptive learning system. In order to visually present the learning situation of students, concept maps were automatically excavated to provide personalized guidance for different students in [45]. [46] used direct hashing and pruning algorithms to construct concept maps from student answer data.…”

Section: Concept Map and Weak Concept Diagnosismentioning

confidence: 99%

Personalized Learning Path Recommendation Based on Weak Concept Mining

Diao

Zeng

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Discovering valuable learning path patterns from learner online learning data can provide follow-up learners with effective learning path reference and improve their learning experience and learning effects. In this paper, a personalized learning path recommendation method based on weak concept mining is proposed. Firstly, according to the degree of concept mastery of historical learners, concept maps of different types of learners are generated by clustering and association rule mining algorithms. A set of weak concept learning paths are then automatically generated through topological sorting algorithm. Secondly, the long short-term memory neural network based on the attention mechanism (LSTM+attention) is trained to predict the learning effect of the weak concept learning path. Finally, the personalized weak concept path that meets the expected learning effect is selected from the path prediction results. In the experiment, the proposed method is not only compared with the traditional recommendation method, but also, a comparative experiment on the impact of different learning effect prediction models is carried out. The experiment results show that our proposed method has obvious advantages in recommendation performance.

show abstract

“…To our knowledge, plenty of work of automatically constructed concept map has been studied with data mining techniques. For example, Tsenga et al (2007), Lee et al (2009) and Qasim et al (2013) leverage association-rule mining; Chen et al (2008), Lau et al (2009) and Huang et al (2006Huang et al ( , 2015 base on text mining; and Marian and Keyword extraction Maria (2009) and Chu et al (2007) design specific algorithms. However, the majority of those methods are domain-specific, e.g.…”

Section: Related Workmentioning

confidence: 99%

Application of keyword extraction on MOOC resources

Jiang

Chen

2017

IJCS

View full text Add to dashboard Cite

Purpose-Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well. Design/methodology/approach-Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels. Findings-From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform. Research limitations/implications-Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

show abstract

Classification-Based Approach to Concept Map Generation in Adaptive Learning

Cited by 7 publications

References 9 publications

Adaptive learning: a cluster-based literature review (2011-2022)

Adaptive learning: a cluster-based literature review (2011-2022)

Personalized Learning Path Recommendation Based on Weak Concept Mining

Application of keyword extraction on MOOC resources

Contact Info

Product

Resources

About