Enhancing the Performance of Multiple Intelligence Learning Styles Prediction in e-Learning Systems Using Machine Learning Techniques

In the context of multiple intelligences theory, intelligent data analysis of learning styles was based on rough set theory

The ability to detect learners’ learning styles based on their learning behaviors is of utmost importance for online educational systems, as it greatly enhances student engagement, motivation, and overall learning outcomes. Knowing the learning preferences of learners may significantly aid in creating personalized learning recommendations and empower learners to identify their own learning styles. However, learners exhibit diverse behaviors in an online setting, which poses significant difficulties in detecting their learning style. This paper proposes a novel approach for detecting learning styles using graph representation learning techniques and machine learning algorithms. While our approach is not reliant on a particular learning style model, our approach may be divided into two distinct parts. Initially, we represented the behavior of learners as a bipartite graph and then transformed this graph into a lower-dimensional representation using the graph embedding approach. This lower-dimensional representation was then utilized for machine learning tasks. Furthermore, we categorize the encoded learner’s sequence using a clustering technique based on the selected learning style model. Our methodology uses the Felder-Silverman model as the learning style model and the Fuzzy C-means algorithm as a clustering technique. Our approach was evaluated using the 2015 KDD Cup dataset through a series of comprehensive experiments to showcase its effectiveness. The findings demonstrate that our approach surpasses the previous approach, achieving an average precision of 0.8737 and an accuracy of 0.9182.

This paper compares the theories of multiple intelligences and learning styles to suggest ways that teachers using a combination of both theories may be able to improve student learning over the range of intelligences. The author proposes a research format for the benefit of future research.

The paper presents a module for studying of learning styles integrated in Moodle. The module is based on the Kolb's Learning Styles Inventory, Felder-Silverman Learning Styles Model, Gardner's Multiple Intelligence Inventory, Reid model. Also some results of pilot study with the module are performed.

<p><strong><em>Abstract—A </em></strong><strong>learning style is an issue related to learners. In one way or the other, learning styles could assist learners in their learning activities. If the learners ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in automatically detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approaches since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposed. Agent learning performs four phased activities, i.e. initialization, learning, matching and recommendations to decide which learning styles are used by the students. Furthermore, the system will provide teaching materials which are appropriate for the detected learning style. The detection process is performed automatically by combining data-driven and literature-based approaches. The detected learning style used for this research is VARK (Visual, Auditory, Read/Write, and Kinesthetic). This learning style detection model is expected to optimize the learners in adhering with the online learning.</strong><strong></strong></p>

The notion of “multiple intelligences” (MI) was established by Howard Gardner, a psychologist from the United States, during the latter part of the 1970s and the early part of the 1980s. Gardner introduced the concept of MI in his 1983 publication titled Frames of Mind: The Theory of Multiple Intelligences, positing that individuals possess unique cognitive abilities across eight discrete domains. As per the theory, there exist nine discrete categories of intelligence, such as logical-mathematical, visual-spatial, musical-rhythmic, verbal-linguistic, bodily-kinesthetic, intrapersonal, interpersonal, existential, and naturalistic. Individuals construct their distinct cognitive frameworks by engaging in activities that are highly valued within their respective cultural contexts. The present article furnishes a comprehensive outline of the rationales and plausible scenarios for deliberating MI theory with pre-service educators during their teacher training. This study examines the significance of Universal Design for Learning (UDL) and comparable models in the context of teacher training, taking into account the distinctions between the semantic theoretical foundations of intelligence in Multiple Intelligences (MI) theory and learning styles theory.

Having a healthy baby is a dream for mothers. However, the high rate of maternal and fetal mortality is still a serious problem, so more accurate fetal health monitoring is needed to prevent pregnancy complications. One of the devices used is Cardiotocography (CTG), which produces data on fetal conditions. The CTG dataset used in this study faces challenges in the form of class imbalance and a high number of features, which can reduce classification performance. This study aims to overcome these challenges by implementing the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) technique for class balancing and Recursive Feature Elimination (RFE) for feature selection. The dataset used is "Fetal Health Classification" from the Kaggle platform, which consists of 2,126 data with three classes: Normal, Suspect, and Pathological. The test results show that the RFE method is able to reduce the number of features from 22 to 18, while SMOTE increases the proportion of minority data. The model built produces good classification performance with an accuracy value of 95%, precision 93%, recall 89%, and F1-score 91%. The ROC-AUC value for the Normal class is 0.9881, Suspect 0.9789, and Pathological 0.9985. Although the model is able to predict the Normal and Pathological classes with high accuracy, the performance on the Suspect class still needs to be improved. Overall, the integration of Random Forest with SMOTE and RFE has proven effective in improving the accuracy of fetal health classification.

Each learner has their own preferences in the learning process. Differences in preferences are closely related to the learning style of each learner. Personalization of e-learning is an overview of online learning that has been customized content based on learning styles of each learner. Detecting learning style needs a technique that is effective and accurate. This study combines literature based method with Support Vector Machine (SVM) to detect students' learning styles. The data used is learning log data of Data Structures and Algorithms class at the Faculty of Computer Science, Universitas Indonesia. The test results showed that SVM has better accuracy compared to Naive Bayes.

Education Technology advances many aspects of learning. More and more learning is taking place online. Learners’ learning behaviors, style, and performance can be easily profiled through learning analytics which collects their online learning footage. It enables and encourages educational research, learning software application development, and online education practices towards personalized and adaptive learning. As we continue to see personalized and adaptive learning progress, we must also pay attention to the negative impacts that feed into our research. In this paper, we will present our introspection of personalized and adaptive learning and argue that it is the social and moral responsibility of educators and institutions to apply personalized and adaptive learning wisely in their education practice. Educators and institutions should also recognize the realistic diversities of individual students’ learning styles and variable learning progress, contextually dependent learning accessibility, and their correspondent support needs for the fine-grained learning activities. We argue that the strategically balanced practices and innovated learning technology are crucial towards an optimized learning experience for the learners.

Student modeling and context modeling play an important role in adaptive and smart learning systems, enabling such systems to provide courses and recommendations that fit students’ characteristics and consider their current context. In this chapter, three approaches are presented to automatically analyze learners’ characteristics and courses in learning systems based on learners’ cognitive abilities, learning styles, and context. First, a framework and a system are presented to automatically identify students’ working memory capacity (WMC) based on their behavior in a learning management system. Second, a mechanism and an interactive tool are described for analyzing course contents in learning management systems (LMSs) with respect to students’ learning styles. Third, a framework and an application are presented that build a comprehensive context profile through detecting available features of a device and tracking the usage of these features. All three approaches contribute toward building a foundation for providing learners with intelligent, adaptive, and personalized support based on their cognitive abilities, learning styles, and context.

Learning style is an important factor that accounts for the individual student learning in any learning environment. Every student has a different learning style and different ways to percept, process, retain and understand new information. In this paper, a new approach is proposed to classify students learning style automatically and dynamically depending on their learning behavior in a learning management system (LMS). There have been several approaches proposed for automatic learning style detection. One of the widely accepted and frequently used classification techniques is the decision tree classifier. The decision tree classifier mainly depends on the construction of strong decision rules which are required to identify learning styles accurately. The lack of strong decision rules would lead to the misclassification of individual students learning style. Hence, this paper mainly focuses on the construction of strong decision rules to strengthen the existing decision tree classifier to accurately and precisely classify students learning style thereby improving the classification accuracy. The proposed approach has experimented with an average of 300 students enrolled for the online courses in Moodle LMS. Initially, the students' behavior are extracted from the web log files of LMS and then preprocessed to build decision tree classifier using strong decision rules based on the three learning dimensions of standard Felder Silverman learning style model (FSLSM). The evaluation result is obtained using the inference engine with forward reasoning searches of the rules until the correct learning style is determined. Based on the result obtained, the prediction of learning style is done for the new students automatically and accurately using the significant rules built in the decision tree classifier. The experimental result proved that the processing dimension shows variance in classification whereas perception and input dimension shows less variance with an average accuracy of 87%.

Software Defect Prediction (SDP) is essential for improving software quality during testing. As software systems grow more complex, accurately predicting defects becomes increasingly challenging. One of the challenges faced is dealing with imbalanced class distributions, where the number of defective instances is significantly lower than non-defective ones. To tackle the imbalanced class issue, use the SMOTE technique. Random Forest as a classification algorithm is due to its ability to handle non-linear data, its resistance to overfitting, and its ability to provide information about the importance of features in classification. This research aims to evaluate important features and measure accuracy in SDP using the SMOTE+RFE+Random Forest technique. The dataset used in this study is NASA MDP D", which included 12 data sets. The method used combines SMOTE, RFE, and random forest techniques. This study is conducted in two stages of approach. The first stage uses the RFE+Random Forest technique; the second stage involves adding the SMOTE technique before RFE and Random Forest to measure the accurate data from NASA MDP. The result of this study is that the use of the SMOTE technique enhances accuracy across most datasets, with the best performance achieved on the MC1 dataset with an accuracy of 0.9998. Feature importance analysis identifies "maintenance severity" and "cyclomatic density" as the most crucial features in data modeling for SDP. Therefore, the SMOTE+RFE+RF technique effectively improves prediction accuracy across various datasets and successfully addresses class imbalance issues.

This research was planned to assess and identify the impact of multiple intelligences on students’ learning styles at university level in public sector universities Rawalpindi. The research used two approaches about theoretical frameworks for conducting this study. One was based on multiple intelligences given by Gardner (2011) which was consist of 9 sub dimensions named as linguistic, logical, musical, bodily, kinesthetic, intrapersonal, interpersonal, naturalistic and existentialist. The second theoretical framework was based on students’ learning styles given by Grasha-Riechman (2002) which was consist of six sub dimensions named as, Independent, avoidant, collaborative, dependent, competitive and participatory. The researcher used quantitative approach to conduct the study. The researcher adopted two questionnaires based on theoretical frameworks. One was based on multiple intelligences given by Gardner (2011) which was consist of 9 sub dimensions named as linguistic, logical, musical, bodily, kinesthetic, intrapersonal, interpersonal, naturalistic and existentialist. There were total 45 items, each dimensions were consisting of 5 items. The second theoretical framework was based on students’ learning styles given by Grasha-Riechman (2002) which was consist of six sub dimensions named as, Independent, avoidant, collaborative, dependent, competitive and participatory. Each dimensions were consisting of 10 items and there were 60 items in total. This research was planned to assess and identify the impact of multiple intelligences on students’ learning styles at university level in public sector universities Rawalpindi. The research used two approaches about theoretical frameworks for conducting this study. One was based on multiple intelligences given by Gardner (2011) which was consist of 9 sub dimensions named as linguistic, logical, musical, bodily, kinesthetic, intrapersonal, interpersonal, naturalistic and existentialist. The second theoretical framework was based on students’ learning styles given by Baneshi, Tezerjani, and Mokhtarpour (2014) which was consist of six sub dimensions named as, Independent, avoidant, collaborative, dependent, competitive and participatory. The researcher used quantitative approach to conduct the study. The researcher adopted two questionnaires based on theoretical frameworks. One was based on multiple intelligences given by Gardner (2011) which was consist of 9 sub dimensions named as linguistic, logical, musical, bodily, kinesthetic, intrapersonal, interpersonal, naturalistic and existentialist. There were total 45 items, each dimensions were consisting of 5 items. The second theoretical framework was based on students’ learning styles given by Baneshi et al. (2014) which was consist of six sub dimensions named as, Independent, avoidant, collaborative, dependent, competitive and participatory. Each dimensions were consisting of 10 items and there were 60 items in total. Convenient sampling technique was used to collect the data. After collecting data, the researcher used SPSS to enter the data and run test according to the objectives. The researcher used individual scores and regression analysis in SPSS to achieve the targeted objectives. It is recommended that teachers should encourage students to explore and develop their multiple intelligence in ways that align with their personal interests and strengths. This can help students to become more self-aware and confident learners.

An examination of multiple intelligence domains and learning styles of pre-service mathematics teachers: Their reflections on mathematics education

Due to the widespread of online learning, learning management systems (LMSs) contain many of online courses but very little attention is paid to how well these courses actually support learners. Teachers build courses according to their preferred teaching methods; on the other hand, learners have different learning styles. The harmony between the learning styles that a course supports and the actual learning styles of students can help to magnify the efficiency of the learning process. In this chapter, an interactive tool is presented for analyzing existing course contents in learning management systems based on learning styles. This tool allows teachers to be aware of the course support level for different learning styles. It visualizes the suitability of a course for students’ learning styles and helps teachers to improve the course support level of their courses. It aims at supporting teachers in adaptive and personalized learning environments to decide-making efficient modifications in the course structure in order to meet the need of different students’ learning styles.KeywordsInteractive course analyzerLearning stylesLearning management systems