Clustering Students Based on Student’s Performance - A Partial Least Squares Path Modeling (PLS-PM) Study

Jayabal, Yogalakshmi; Ramanathan, Chandrashekar

doi:10.1007/978-3-319-08979-9_29

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Most education platforms attempt to plan reasonable learning paths for college student users, but they have generally ignored differences in their learning time distribution preferences, learning habits, and learning requirements, and haven't taken the dynamic development trends of their learning states into consideration. Moreover, the generated learning path often does not conform to the cognition sequence of college students, and is not easily accepted or recognized by them, sometimes it can even lead to problems such as cognitive overload, disorientation, and learning efficiency in them [16][17][18][19][20][21][22][23][24]. Therefore, rationally planning the learning path for the personalized and fragmented learning of college students is a very meaningful and practical work.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for the Fragmented Learning of College Students Based on Artificial Intelligence

Li¹

2022

Int. J. Emerg. Technol. Learn.

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Most education platforms attempt to plan reasonable learning paths for college student users, but they have generally ignored differences in their learning time distribution preferences, learning habits, and learning requirements, and haven’t taken the dynamic development trends of their learning states into consideration. To make up for these shortcomings, this paper aims to study the learning path planning for the fragmented learning of college students based on artificial intelligence. At first, this paper proposed a fragmented learning path planning model for college students, and introduced a fragmented knowledge concept map method to mine the recommended fragmented knowledge structure and the association between concepts, so as to fragment knowledge concept into small knowledge pieces and realize fragmented knowledge recommendation. Then, this paper introduced the regularization constraints, and adopted the sparse auto-encoder to predict the missing values in the interaction matrix, so as to solve the problem of data sparsity. After that, hybrid recommendation of fragmented knowledge was made based on the multi-layer perception network model, and the accuracy of the personalized recommendation of fragmented knowledge had been effectively improved. At last, experimental results verified the effectiveness of the proposed recommendation model.

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Section: Introductionmentioning

confidence: 99%

Path Planning for the Fragmented Learning of College Students Based on Artificial Intelligence

Li¹

2022

Int. J. Emerg. Technol. Learn.

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“…are available nowadays. Among them, k-means proposed in MacQueen (1967) has been widely-used in many works (Adjei et al, 2017;Bresfelean et al, 2008;Campagni, Merlini, & Verri, 2014;Jovanovic et al, 2012), while the Partitional Segmentation algorithm in Jayabal and Ramanathan (2014) and the FANNY and AGNES algorithms in Kerr and Chung (2012). Although efficient and effective in some cases, k-means is influenced by the existence of noises and only its intra-cluster compactness is taken into account.…”

Section: Introductionmentioning

confidence: 99%

A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

Nguyen

2019

Journal of Information and Telecommunication

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Exploiting single models for a better ensemble one is popular for a classification or clustering task. For educational data clustering, our work proposes a kernel-induced weighted object-cluster association ensemble method, named kWOCA, to discover the inherent structure of the data and return the student clusters of higher quality. kWOCA is an advanced version that can automatically determine the number of desired clusters based on Bayesian Information Criterion. It also conducts consensus clustering in the feature space for non-linearly separated clusters where the discrimination between the objects is captured from the base clusterings. Besides, it encodes the differences between the clusters in each base clustering and those between the base clusterings in the ensemble when making a synthesis of the base clusterings. A kernel-induced weighted object-cluster association matrix is defined to store such rich information. Using this matrix, kWOCA outperforms the existing clustering ensemble methods. Experimental results on the real educational data sets and the benchmark Iris data set show the better effectiveness of kWOCA with higher Normalized Mutual Information values. As a result, groups of the most similar students based on their study performance can be discovered better. These resulting student clusters can be then further analyzed for academic affairs.

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“…For example, Bogarín et al [4] used data of the undergraduate students in an online course using Moodle and Zakrzewska [26] got data about the undergraduate and graduate students participating in the experiments in online collaboration also on Moodle, Refs. [5,11,20] clustered the data of undergraduate students and courses in a few years, Jayabal and Ramanathan [12] analyzed the 10th grade data, Kerr and Chung [13] extracted the student performance features from log data in educational video games and simulations, Li and Yoo [15] used the data recorded from each student's actual lab experi-ence, and so on. Among these related works, only Inyang and Joshua [11] has presented the handling of incomplete data by deleting the missing results in the courses while the others had no mention of incomplete data issues.…”

Section: Introductionmentioning

confidence: 99%

“…As one of the widely used mining tasks, educational data clustering has been considered with many different student-related aspects in [4,5,[11][12][13]15,18,[20][21][22]26] for many various purposes. For example, discovering the groups of similar students is based on study performance in [11], learning behavior in [15], skill in [18], preference in [26], etc.…”

Section: Introductionmentioning

confidence: 99%

Making kernel-based vector quantization robust and effective for incomplete educational data clustering

Nguyen

2016

Vietnam J Comput Sci

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Nowadays, knowledge discovered from educational data sets plays an important role in educational decision making support. One kind of such knowledge that enables us to get insights into our students' characteristics is cluster models generated by a clustering task. Each cluster model presents the groups of similar students by several aspects such as study performance, behavior, skill, etc. Many recent educational data clustering works used the existing algorithms like k-means, expectation-maximization, spectral clustering, etc. Nevertheless, none of them considered the incompleteness of the educational data gathered in an academic credit system although incomplete data handling was figured out well with several different general-purpose solutions. Unfortunately, early in-trouble student detection normally faces data incompleteness as we have collected and processed the study results of the second-, third-, and fourth-year students who have not yet accomplished the program as of that moment. In this situation, the clustering task becomes an inevitable incomplete educational data clustering task. Hence, our work focuses on an incomplete educational data clustering approach to the aforementioned task. Following kernel-based vector quantization, we define a robust effective simple solution, named VQ_fk_nps, which is able to not only handle ubiquitous data incompleteness in an iterative manner using the nearest prototype strategy but also optimize the clusters in the feature space to reach the resulting clusters with arbitrary shapes in the data space. As shown through the experimental results on real educational data sets, the clusters from our solution have better cluster quality as compared to some existing approaches.

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Clustering Students Based on Student’s Performance - A Partial Least Squares Path Modeling (PLS-PM) Study

Cited by 10 publications

References 4 publications

Path Planning for the Fragmented Learning of College Students Based on Artificial Intelligence

Path Planning for the Fragmented Learning of College Students Based on Artificial Intelligence

A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

Making kernel-based vector quantization robust and effective for incomplete educational data clustering

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