On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018

Charitopoulos, Angelos; Rangoussi, Maria; Koulouriotis, Dimitrios E.

doi:10.1007/s40593-020-00200-8

Cited by 58 publications

(46 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Learning analytics has been widely researched and used in higher education institutions, especially due to the maturity level of adopting data analysis tools in these institutions ( Leitner et al, 2017 ; Waheed et al, 2018 ; Charitopoulos et al, 2020 ). However, despite some promising results, learning analytics does not have the same level of adoption in other educational contexts, such as high schools ( Cechinel et al, 2020 ; Ifenthaler, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

Learning analytics aims to analyze data from students and learning environments to support learning at different levels. Although learning analytics is a recent field, it reached a high level of maturity, especially in its applications for higher education. However, little of the research in learning analytics targets other educational levels, such as high school. This paper reports the results of a systematic literature review (SLR) focused on the adoption of learning analytics in high schools. More specifically, the SLR followed four steps: the search, selection of relevant studies, critical assessment, and the extraction of the relevant field, which included the main goals, approaches, techniques, and challenges of adopting learning analytics in high school. The results show that, in this context, learning analytics applications are focused on small-scale initiatives rather than institutional adoption. Based on the findings of this study, in combination with the literature, this paper proposes future directions of research and development in order to scale up learning analytics applications in high schools.

show abstract

Section: Introductionmentioning

confidence: 99%

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Their core advantage is the generation of numerous distribution free and robust models, among which the most adequate and optimal model in a given sense can be selected. The following ML methods are widely used to model educational data: logistic regression, cluster analysis, decision trees (CART), support vector machines (SVM), multivariate adaptive regression splines (MARS), random forest (RF), neural networks (NN), fuzzy logic and others [10,24].…”

Section: Machine Learning Methods Used For Statistical Analysesmentioning

confidence: 99%

“…As part of the family of regression trees, the CART and CART-EB methods we use can successfully deal with uncertainty, qualitatively stated problems and incomplete, imprecise or even contradictory data sets, as stated in [10]. These can process both nominal and numerical data, handle multidimensional and multivariety data, easily identify patterns and nonlinear complex relationships between the predictors, thus facilitating the interpretation of models.…”

mentioning

confidence: 99%

Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS

2020

View full text Add to dashboard Cite

The aim of this study is to evaluate students’ achievements in mathematics using three machine learning regression methods: classification and regression trees (CART), CART ensembles and bagging (CART-EB) and multivariate adaptive regression splines (MARS). A novel ensemble methodology is proposed based on the combination of CART and CART-EB models in a new ensemble to regress the actual data using MARS. Results of a final exam test, control and home assignments, and other learning activities to assess students’ knowledge and competencies in applied mathematics are examined. The exam test combines problems on elements of mathematical analysis, statistics and a small practical project. The project is the new competence-oriented element, which requires students to formulate problems themselves, to choose different solutions and to use or not use specialized software. Initially, empirical data are statistically modeled using six CART and six CART-EB competing models. The models achieve a goodness-of-fit up to 96% to actual data. The impact of the examined factors on the students’ success at the final exam is determined. Using the best of these models and proposed novel ensemble procedure, final MARS models are built that outperform the other models for predicting the achievements of students in applied mathematics.

show abstract

“…PPMI-SVD, on the other hand, uses the truncated singular value decomposition (SVD) technique to achieve dimensionality reduction, aimed at finding the maximum features to describe the semantics of words and the retained dimensional features to approximate the PPMI matrix composed of wordcontext information [6]. Charitopoulos et al point out that the SVD rank reduction algorithm does not guarantee the nonnegativity of the matrix decomposition, so nonnegative matrix factorization (NMF) becomes a sensible choice for decomposing the PPMI, which guarantees the nonnegativity of the dimensional approximate reduction of the semantic relationship of the word-context information and is more consistent with the semantic relationship hypothesis [7]. Raveh et al demonstrated that matrix factorization methods are consistent with the word vectors of neural network language models in terms of task performance, such that PPMI-SVD matrix decomposition is equivalent to skipgram (SGNS) based on negative sampling [8].…”

Section: Current Status Of Researchmentioning

confidence: 99%

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

Liu

Tsai

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.

show abstract

On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018

Cited by 58 publications

References 42 publications

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

Contact Info

Product

Resources

About