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Open AccessJournal ArticleDOI

Dynamic Group Formation With Intelligent Tutor Collaborative Learning: A Novel Approach for Next Generation Collaboration

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TLDR
In this paper, an activity-based dynamic group formation technique is proposed to supplement collaborative group formation with a collaborative platform, where initial groups are formed based on students learning styles and knowledge level.
Abstract
Group Formation (GF) strongly influences the collaborative learning process in Computer-Supported Collaborative Learning (CSCL). Various factors affect GF that include personal characteristics, social, cultural, psychological, and cognitive diversity. Although different group formation methods aim to solve the group compatibility problem, an optimal solution for dynamic group formation is still not addressed. In addition, the research lacks to supplement collaborative group formation with a collaborative platform. In this study, the next level of collaboration in CSCL and Intelligent Tutoring System (ITS) platforms is achieved. First, initial groups are formed based on students learning styles, and knowledge level, i.e. for knowledge level, an activity-based dynamic group formation technique is proposed. In this activity, swapping of students takes place on each permutation based on their knowledge level. Second, the formed heterogeneous balanced groups are used to augment the collaborative learning system. For this purpose, a hybrid framework of Intelligent Tutor Collaborative Learning (ITSCL) is used that provides a unique and real-time collaborative learning platform. Third, an experiment is conducted to evaluate the significance of the proposed study. Inferential and descriptive statistics of Paired T-Tests are applied for comprehensive analysis of recorded observations. The statistical results show that the proposed ITSCL framework positively impacts student learning and results in higher learning gains.

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Journal ArticleDOI

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

TL;DR: A novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data that outperforms previously developed models.
Journal ArticleDOI

A Parametrized Comparative Analysis of Performance Between Proposed Adaptive and Personalized Tutoring System “Seis Tutor” With Existing Online Tutoring System

- 01 Jan 2022 - 
TL;DR: Seis Tutor as discussed by the authors is a face-to-face tutoring system that offers a learning environment that best suits the learner's preferences (learning styles) and grasping levels (learning levels).
Journal ArticleDOI

A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

TL;DR: A hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied and demonstrates that the proposed model is superior to the performance of the other comparison approaches.
Journal ArticleDOI

A Parametrized Comparative Analysis of Performance between Proposed Adaptive and Personalized Tutoring System “Seis Tutor” with Existing Online Tutoring System

TL;DR: This paper has detailed the architecture of Seis Tutor system and compared it with other existing traditional tutoring systems, i.e., My Moodle, Course-Builder, and Teachable.
References
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Learning and Teaching Styles in Engineering Education.

TL;DR: A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages.
Journal ArticleDOI

Social and cognitive factors driving teamwork in collaborative learning environments : team learning beliefs and behaviors

TL;DR: A team is more than a group of people in the same space, physical or virtual as discussed by the authors, and increasing attention has been devoted to the social bases of cognition, taking into consideration how...
Journal ArticleDOI

Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence

TL;DR: Structural equation modelling results revealed that student enrolment has a positive impact on social presence and cognitive presence, and Enrolment also positively influences learning performance through the above two presences.
Book ChapterDOI

The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains

TL;DR: Preliminary small-scale controlled experiments involving basic Cognitive Tutor development tasks found efficiency gains due to CTAT of 1.4 to 2 times faster, and it is expected that continued development of CTAT will lead to further efficiency gains.
Journal ArticleDOI

A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics

TL;DR: A method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups, which allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization.
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