A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education

Li, Qingyun; Duffy, Peter L.; Zhang, Zhongyang

doi:10.3390/systems10040096

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…In the proposed methodology, deep learning techniques are adopted for the classification. The adopted deep learning models for the classification are Bayesian, CNN and LSTM (Li et al, 2022b). The purpose of these deep learning models is discussed below:…”

Section: Workflow Of Proposed Methodologymentioning

confidence: 99%

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

Sudhamathy,

Valliammal

2023

Int. J. Appl. Sci. Eng.

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Learning analytics (LA) is a research domain that leverages the analysis of data from the learning process to gain a deeper understanding and enhance learning outcomes. To classify learner performance, a model has been proposed that combines various deep learning techniques, including convolutional neural network (CNN), Long Short-Term Memory (LSTM), and Bayesian models. The integration of these approaches aims to improve the accuracy and effectiveness of performance classification. CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. The effective classification of learners' performance is achieved by combining the strengths of CNN and LSTM, along with the integration of a Bayesian deep learning model. The performance of the proposed model is estimated using the metrics like Accuracy, Precision, Recall and F1-Score. The model showed improvements in Accuracy, Precision, Recall and F1-Score are 98.18%, 97.09%, 96.38% and 95.35% respectively. The proposed model is compared with another existing model such as LSTM and collaborative machine learning (ML) models in terms of performance metrics. The proposed method attained accuracy of 98.18% which is higher than other existing models.

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Section: Workflow Of Proposed Methodologymentioning

confidence: 99%

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

Sudhamathy,

Valliammal

2023

Int. J. Appl. Sci. Eng.

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“…Personalized learning systems focus on delivering content based on learner suitability, preferences, knowledge, and cognition level [12]. Data warehouses (DW) have been used in certain research to examine the ways students make decisions while utilizing the learning management system (LMS) to further decide based on their assessment [13,14]. Using online tools and resources like LMS, students obtain a degree through distant learning after passing the examination [15].…”

Section: Introductionmentioning

confidence: 99%

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

2024

IJATEE

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Learning analytics (LA) is the measuring, gathering, analyzing, and reporting of data on learners and their environments. This data is used to analyze and improve e-learning. LA has traditionally been used for a variety of purposes, including the prediction of student academic progress and more specifically, the identification of students who are in danger of failing a course or quitting their studies. However, the majority of the existing schemes have the issue of accurately predicting the students' performance in online courses. In this paper, correlation-based self-attention networklong short-term memory (SAN-LSTM) is proposed to predict students' outcomes, along with the effectiveness of the teaching experience, as well as the assessment methods. Initially, the data is collected from three datasets namely, WorldUC, Liru, and Junyi to evaluate the performance of the proposed approach. The min-max normalization is employed to improve the performance of the approach. The correlation-based feature selection (CFS) is employed to select appropriate features from the pre-processed data. Finally, the correlation-based SAN-LSTM is established to forecast the effectiveness of fine-grained learning. Three real-world datasets gathered from various e-learning empirically validated that the proposed model improves prediction outcomes and provides useful data for formative evaluation. The existing methods such as adaptive sparse self-attention network (AS-SAN), Bangor engagement metric (BEM), and deep belief network learning style (DBNLS) are used for comparison to justify the effectiveness of the correlation-based SAN-LSTM method. The proposed correlation-based SAN-LSTM achieves better results of 98% of accuracy and 93% of precision. The proposed method achieves 98% accuracy which is higher when compared to those of AS-SAN, BEM, and DBNLS.

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Uzaktan eğitim sistemi için veri ambarı altyapısının geliştirilmesi

Güneş

BİRGİN

2023

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The use of a data warehouse (DW) in e-learning applications helps us evaluate students from different perspectives. Depending on user behavior, we may change the content or appearance of the e-learning platform to achieve the best results. The aim of this study is to build a data warehouse infrastructure, which is a central information system for Anadolu University's distance education system. Thanks to the data warehouse project, all learners’ activities in the Learning Management System (ÖYS: Anadolum e-Kampüs) will be recorded regularly. Various analytical reports were obtained about students' study styles, preferred e-learning resources, and the time spent in the system. Additionally, the data warehouse system regularly pulls information from other information systems, such as the student information system related to the open education system. Data warehousing is designed to combine data from multiple sources into a single unified view, rather than performing operational operations like a database. Generally, data warehouses are used for analytical processing as opposed to transaction processing. They are used to analyze historical data and trends, as well as to identify patterns and insights that can aid in informing business decisions. When analysis and reporting is done in a different environment using data warehousing, there is no additional load on the database, which is already busy with daily operational processes. The data warehouse has made it easy to obtain student analytics for various periods, including daily, monthly, and yearly. The reports provided through this infrastructure are used in studies to increase the quality of the LMS system.

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A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education

Cited by 4 publications

References 30 publications

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

Uzaktan eğitim sistemi için veri ambarı altyapısının geliştirilmesi

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