An Adaptive M-Learning Usability Model for Facilitating M-Learning for Slow Learners

Despite the explosion of interest in adaptive learning and learning analytics (LA) for higher education (HE), there has been relatively little research integrating educational approaches’ indicators to build an adaptive learning analytics model. Adaptive learning analytics (ALA) models have grown in favor of HE due to their claims of enhancing student learning outcomes, providing personalized learning paths, and allowing students to interact with course material at their own pace. With focus on using data to personalize the learning experience and the environment in which the experience of learning occurs, LA centers on enhancing education through meticulous data analysis, while big data (BD) in education addresses the overarching challenges and opportunities arising from extensive and varied datasets. These concepts are interconnected, where LA and ALA specifically apply BD principles within the educational context. In this paper, we explain some BD concepts used in HE, define the essential perceptions related to LA, and analyze educational approaches to define the fundamental implications. Besides, we try to connect some LA model with educational approaches based on the big educational data collected in order to establish an efficient educational model. We include all steps cited before we try to build an ALA model in HE that resolves the limitations of the oldest models, thus improving the learner’s learning process by adding and treating additional indicators.

Section: Decisional Processmentioning

confidence: 99%

Big Data Analytics in Higher Education: A New Adaptive Learning Analytics Model Integrating Traditional Approaches

Bellaj,

Ben Dahmane,

Boudra

et al. 2024

“…Specifically, based on the different layers of encoded information, it is divided into node-level encoding and graph-level encoding. Assuming the set of whole graph node feature vectors at layer m is represented by G (1) , the feature vector of the node to be updated at layer m is denoted by g n m , the feature vector of the neighbor node at layer m is denoted by g i m , and the corresponding edge feature is denoted by r ni .…”

Section: Extraction Of Mobile Local Influence Subgraphsmentioning

confidence: 99%

“…In the contemporary era marked by the swift evolution of information technology, mobile interaction technologies have deeply permeated various facets of daily life and work, exerting a particularly notable impact on educational models [1,2]. With the widespread adoption of smartphones and tablets, mobile learning, as an innovative form of learning, has provided learners with significant convenience in terms of time and space [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Exploring Pathways for Mobile Interaction Technologies to Foster Innovation in Entrepreneurial Education Models

2024

With the rapid advancement of information technology, mobile interaction technologies have emerged as a pivotal force driving educational innovation, especially in the realm of entrepreneurial education. These technologies have not only transformed the methods of acquiring and sharing knowledge but have also facilitated interaction and collaboration among learners. This investigation aims to explore how mobile interaction technologies can foster innovation in entrepreneurial education models. The focus is specifically placed on the construction of a mobile interaction-based collaborative learning team environment model oriented towards knowledge building as well as on identifying collaborative strategies that can effectively enhance entrepreneurial learning teams based on mobile localized influence networks. It has been discovered that optimizing mobile learning environments and collaborative strategies improves the quality of interaction and learning outcomes among teams, providing new pathways for entrepreneurial education. Existing research, however, lacks a comprehensive discussion on the deep integration of mobile interaction technologies with entrepreneurial education, especially in terms of systematic studies on team collaboration and the process of knowledge construction. Through theoretical exploration and empirical analysis, this manuscript aims to bridge the research gap, providing theoretical and practical support for innovation in entrepreneurial education models. This endeavor holds significant research value and practical implications.

“…Among the strategies explored, AI-based adaptive learning stood out as a potential answer [2]- [4]. Each student's unique demands are satisfied by this approach, which uses technology and individualized learning paths to adapt the learning process to fit their needs and preferences [5]- [7]. The aim is to reignite students' excitement for studying and create an atmosphere where they may succeed academically.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Implementing a Moodle Plug-in as an AI-based Adaptive Learning Solution on Learning Effectiveness: Case of Morocco

Ezzaim,

Dahbi,

Haidine

et al. 2024

This article presents feedback on the implementation of an Artificial Intelligence-based adaptive learning Moodle plugin aimed at enhancing the engagement levels and academic performance of 102 Moroccan high school students. The primary objective of this study was to assess and compare the performance of students utilizing the adaptive learning system with those employing conventional learning methods. To guarantee the efficacy of this approach, a participant satisfaction survey and a comprehensive summative evaluation were conducted, revealing the positive impact of AI-based adaptive learning on the participants. The results of this study highlight the potential benefits of integrating AI-driven adaptive learning into high school computer science curricula, emphasizing how it may raise student engagement and academic performance. These results strengthen the determination to use this teaching methodology with students in future educational activities.