Evaluating students’ experiences in self-regulated smart learning environment

The increasing development in smart and mobile technologies transforms a learning environment into a smart learning environment that can support diverse learning styles and skills development. An online learner needs to be supported for an engaging and active learning experience. Previously, this progressive research developed and implemented a self-regulated smart learning environment (mobile app) among final-year undergraduate students to support online learning experiences. To understand students' experiences, there is a need to evaluate the mobile app. However, there is a lack of a well-documented study investigating students' experiences in terms of usability, challenges, and factors influencing satisfaction to inform a decision regarding future implementation. This study attempts to fill these gaps by exploring these experiences for sustainable future implementation. The study used cyclical mixed-method evaluations to explore the experiences of 85 final-year undergraduate students. The quantitative data were collected using a survey on the constructs of the research model previously developed to evaluate factors influencing students' satisfaction, and the qualitative used focus group discussions to explore usability experiences and challenges of implementations. The quantitative data were analyzed using SPSS 25 to confirm the structural equation model's relationship. The qualitative data were analyzed using a thematic process to understand students' experiences. The findings from the first mixed-method evaluation show that students were able to follow the learning process, and the application supported their online learning experiences. However, a student expressed the need to improve user functionalities to motivate and engage them in the learning process. The suggestions were incorporated into the mobile app development for the second evaluation. The findings from the second evaluation revealed similar support. However, students suggested a web-based version to support different operating systems and improve interactions. Furthermore, the information system qualities and moderating factors investigated supported students' satisfaction. Future research could explore facilitators' experiences in the mobile app for sustainable development and implementation for engaging online learning experiences and skills development

Teachers’ Perceptions and Undergraduate Students’ Experience in E-Exam in Higher Institution in Nigeria

This study was conducted to explore teachers’ perceptions, and students’ experiences in e-Examination in University of Maiduguri. Questionnaires were distributed to 30 teachers and 50 students, and the 80 collated instruments were valid for data analysis, representing a response rate of 100%. The validity of the questionnaire was approved by some experts in the field. Descriptive statistics was used to analyzed the data. The descriptive results indicated that teachers’ and students’ exposure and experiences to ICT was low. The findings further revealed that both the teachers and students agreed that e-Examination is important to them and more efforts should be gear toward improving its integrity. Furthermore, both the teachers and students agreed that e-Examination is not the true reflection of the students’ performance if used as the only way of measurement.From the findings of the study, it is recommended that courses such as computer supported learning, and  e-Examination process, should be introduced periodically for teachers and students’ exposure to the nature of e-Examination through practice and drill to improve teachers’ and students’ level of confidence and perceptions towards the use of e-Examination. Keywords: Teachers, Students, ICT, e-examination, higher education, institutio

Designing a Mobile Learning System for Higher Education: A Literature Review

The proliferation of portable electronic devices and wireless networking is creating a change from e-learning to m-learning especially in higher education, and it increases collaborations, interactions, anywhere, anytime, student centre learning etc. Furthermore, Mobile learning gives learners possibilities to carry out learning tasks with or without connectivity to a virtual learning environment. Using appropriate communications channels supported by mobile devices, students are able to interact with fellow students as well as with teachers. Additionally, mobile devices support interactive educational systems with emphasis on the learner-to-content interaction. However, in designing learning activities for mobile devices there is a need to establish an appropriate theoretical framework (Baharom, 2013, Thomas, 2005; Collis & Moonen, 2001). The aim of this paper is to present a research framework and literature review for mobile learning environment.  The work is grounded in social constructivist theory, and associated learning theory of Conversational in order to derive attributes.  These attributes are: conversation, collaboration, creativity, content, context, technology, users, and pedagogy (Baharom, 2013; Prasertsilp, 2013; Mohammed and Alameen, 2014). Through these attributes, an iterative framework for a mobile learning environment is proposed to support teachers and instructional designers to create educational values, and provide students with a quality in mobile learning environment. In turn, this will to improve both instructors’ teaching and students’ learning experiences. Primary data collection and analyses will be conducted at Adamawa State University, Mubi, Nigeria to determine students’ readiness and institutional support for a mobile learning intervention

Review on self-regulated learning in smart learning environment

Abstract Despite the increasing use of the self-regulated learning process in the smart learning environment, understanding the concepts from a theoretical perspective and empirical evidence are limited. This study used a systematic review to explore models, design tools, support approaches, and empirical research on the self-regulated learning process in the smart learning environment. This review revealed that there is an increasing body of literature from 2012 to 2020. The analysis shows that self-regulated learning is a critical factor influencing a smart learning environment’s learning process. The self-regulated learning components, including motivation, cognitive, metacognitive, self-efficiency, and metacognitive components, are most cited in the literature. Furthermore, self-regulated strategies such as goal setting, helping-seeking, time management, and self-evaluation have been founded to be frequently supported in the literature. Besides, limited theoretical models are designed to support the self-regulated learning process in a smart learning environment. Furthermore, most evaluations of the self-regulated learning process in smart learning environment are quantitative methods with limited mixed methods. The design tools such as visualization, learning agent, social comparison, and recommendation are frequently used to motivate students’ learning engagement and performance. Finally, the paper presents our conclusion and future directions supporting the self-regulated learning process in the smart learning environment

An Artificial Neural Network (ANN)-based learning agent for classifying learning styles in self-regulated smart learning environment

The increasing development in smart and mobile technologies are transforming learning environments into a smart learning environment. Students process information and learn in different ways, and this can affect the teaching and learning process. To provide a system capable of adapting learning contents based on student's learning behavior in a learning environment, the automated classification of the learners' learning patterns offers a concrete means for teachers to personalize students' learning. Previously, this research proposed a model of a self-regulated smart learning environment called the metacognitive smart learning environment model (MSLEM). The model identified five metacognitive skills-goal settings (GS), help-seeking (HS), task strategies (TS), time-management (TM), and self-evaluation (SE) that are critical for online learning success. Based on these skills, this paper develops a learning agent to classify students' learning styles using artificial neural networks (ANN), which mapped to Felder-Silverman Learning Style Model (FSLSM) as the expected outputs. The receiver operating characteristic (ROC) curve was used to determine the consistency of classification data, and positive results were obtained with an average accuracy of 93%. The data from the students were grouped into six training and testing, each with a different splitting ratio and different training accuracy values for the various percentages of Felder-Silverman Learning Style dimensions

Smart Technologies for Smart Campus Information System

The increasing development of intelligent technologies offer opportunities for objects in the real world to communicate using sensors and communication networks. One of the application areas is the smart campus for the smart information system. The construction of the smart campus based on smart technologies such as Bigdata, cloud computing, mobile computing, network infrastructure needs the understanding and the exploration of these technologies in the development process. This conceptual paper explored the roles of smart technologies in developing a smart campus. Analysis of the key concepts; the architectural layers for the smart campus were proposed hoping to promote smart campus information system for a sustainable intelligent campus. The concept could be a platform for developing smart city in a developing context