Learning Analytics in MOOCs as an Instrument for Measuring Math Anxiety

Dyulicheva, Yulia

doi:10.17323/1814-9545-2021-4-243-265

Cited by 6 publications

(8 citation statements)

References 1 publication

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Section: Feature Modelmentioning

confidence: 99%

“…Accuracy (%) Precision (%) Recall (%) F-Measure (%) [47] 92.82 --- [52] ---94.00 [56] 90.32 -90.86 93.80 [57] 98.01 --99.43 [58] 96.99 --88.72 CNN [61] ---74.00 LSTM [63] 87.95 87.00 87.00 81.00 LSTM [64] 83 In [68] The authors applied LSTM over Glove embedding metrics, where the highest accuracy obtained was 95.80%. Unlike [68], our proposed ORDSAENet model exhibited the accuracy of 95.87%, which is higher than the state-of-art [68]. Moreover, the ability to inculcate Word2Vec embedding in conjunction with SMOTE-ENN resampling and Bi-LSTM feature extraction and learning strengthened ORDSAENet to address varied challenges including data heterogeneity, unstructured-ness, convergence and class-imbalance.…”

Section: Feature Modelmentioning

confidence: 99%

“…And therefore, the aforesaid pre-processing solutions can ensure a robust sentiment annotation model and therefore the answer for the RQ1 is found affirmative. In sync with [68][70], the response of the Word2Vec embedding with SMOTE-ENN resampling followed by Bi-LSTM feature extraction and learning can be effective towards educational sentiment analysis. Thus, the answers for RQ2 and RQ3 too are found affirmative.…”

Section: Feature Modelmentioning

confidence: 99%

“…Thus, the answers for RQ2 and RQ3 too are found affirmative. The other state-of-arts as well such as [68][70] too revealed that the semantic embedding method can yield better feature environment towards educational sentiment annotation solution. The answer for the research question RQ3 is found positive.…”

Section: Feature Modelmentioning

confidence: 99%

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ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

B A,

K N

2023

JMC

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The high pace rising global competitions across education sector has forced institutions to enhance aforesaid aspects, which require assessing students or related stakeholders’ perception and opinion towards the learning materials, courses, learning methods or pedagogies, etc. To achieve it, the use of reviews by students can of paramount significance; yet, annotating student’s opinion over huge heterogenous and unstructured data remains a tedious task. Though, the artificial intelligence (AI) and natural language processing (NLP) techniques can play decisive role; yet the conventional unsupervised lexicon, corpus-based solutions, and machine learning and/or deep driven approaches are found limited due to the different issues like class-imbalance, lack of contextual details, lack of long-term dependency, convergence, local minima etc. The aforesaid challenges can be severe over large inputs in Big Data ecosystems. In this reference, this paper proposed an outlier resilient semantic featuring deep driven sentiment analysis model (ORDSAENet) for educational domain sentiment annotations. To address data heterogeneity and unstructured-ness over unpredictable digital media, the ORDSAENet applies varied pre-processing methods including missing value removal, Unicode normalization, Emoji and Website link removal, removal of the words with numeric values, punctuations removal, lower case conversion, stop-word removal, lemmatization, and tokenization. Moreover, it applies a text size-constrained criteria to remove outlier texts from the input and hence improve ROI-specific learning for accurate annotation. The tokenized data was processed for Word2Vec assisted continuous bag-of-words (CBOW) semantic embedding followed by synthetic minority over-sampling with edited nearest neighbor (SMOTE-ENN) resampling. The resampled embedding matrix was then processed for Bi-LSTM feature extraction and learning that retains both local as well as contextual features to achieve efficient learning and classification. Executing ORDSAENet model over educational review dataset encompassing both qualitative reviews as well as quantitative ratings for the online courses, revealed that the proposed approach achieves average sentiment annotation accuracy, precision, recall, and F-Measure of 95.87%, 95.26%, 95.06% and 95.15%, respectively, which is higher than the LSTM driven standalone feature learning solutions and other state-of-arts. The overall simulation results and allied inferences confirm robustness of the ORDSAENet model towards real-time educational sentiment annotation solution.

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Section: Feature Modelmentioning

confidence: 99%

Section: Feature Modelmentioning

confidence: 99%

Section: Feature Modelmentioning

confidence: 99%

Section: Feature Modelmentioning

confidence: 99%

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ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

B A,

K N

2023

JMC

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“…Following the methodology for detecting mathematical anxiety based on the analysis of MOOC comments outlined in [16], the vectorization of comment texts based on the deep model BERT of the language representation and the k-means clustering algorithm was used to identify clusters.…”

Section: Clustering Text Messages Containing Descriptions Of Affectiv...mentioning

confidence: 99%

Identification of Affective States Based on Automatic Analysis of Texts of Comments in Social Networks

Dyulicheva

2022

Autom Remote Control

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The paper considers the problem of classifying 3553 English-language comments from the social network Reddit based on various approaches to the vectorization of comment texts, including bag of words, TF-IDF, bigrams analysis based on pointwise mutual information (PMI) and sentiments, and the deep model BERT of the language representation. The use of a hybrid approach based on text vectorization using BERT and bigrams analysis have made it possible to improve the quality of comments classification up to 91%. Based on a cluster analysis of 1857 English-language comments describing anxiety, clusters were identified using BERT+k-means. The study proposes a hybrid approach based on the use of the LDA topic modeling method, the VADER sentiments analysis method, pointwise mutual information, and parts of speech analysis and permitting one to select bigrams and trigrams to describe clusters of comments. To visualize the extracted patterns in the form of trigrams, a knowledge graph was constructed that describes the subject area, and a comparison of the words of the selected target trigrams with the words of a custom dictionary describing various affective disorders has made it possible to determine the types of psychosocial stressors associated with affective disorders.

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Development of an AI predictive model to categorize and predict online learning behaviors of students in Thailand

Chonraksuk,

Boonlue

2024

Heliyon

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Learning Analytics in MOOCs as an Instrument for Measuring Math Anxiety

Cited by 6 publications

References 1 publication

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

Identification of Affective States Based on Automatic Analysis of Texts of Comments in Social Networks

Development of an AI predictive model to categorize and predict online learning behaviors of students in Thailand

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