A case study exploring Saudi special education teachers' perceptions toward the use of mobile technology for teaching purposes

Advisors: Wei Chen Hung.Committee members: Rebecca Hunt; Thomas Smith.Includes bibliographical references.Includes illustrations.Within the last few decades special education has benefited from the vast revolution of technologies. These technologies have contributed in facilitating cognitive development as well as learning processes for students with disabilities. One of these emerging technologies is the tablet device and its applications. Due to increasing demands to integrate the latest technology into educational settings, previous studies have looked at the potential of adopting tablet devices and their applications as instruction technology tools in special education classrooms. This case study sought to explore male special education teachers' perceptions toward using tablet devices for teaching purposes in Saudi Arabia. The objectives of this case study were 1) explore male special education teachers' perceptions about using tablet devices for teaching purposes. Their perceptions were measured based on eight variables (voluntariness, relative advantage, compatibility, image, ease of use, trialability, result demonstrability, and visibility) derived from Rogers' and Moore and Benbasat's theories; 2) Examine the impact of teachers' characteristics of age, years of teaching experiences, educational background, and school level being taught on their perceptions; 3) Identify obstacles hindering the adoption of tablet devices for teaching purposes; and 4) Explore the roles of school leadership concerning the use of tablet devices inside the school from the perspectives of special education teachers. This case study employed explanatory sequential mixed methods design, which involves two data collection phases (quantitative → qualitative). The quantitative phase is the primary phase followed by qualitative data collection. The purpose of qualitative phase is to provide further explanation regarding phase one results. A total of 175 participants participated in the survey phase. The results showed the overall perceptions had an overall mean of 3.4 and a standard deviation of (SD = 0.47). In addition, the results revealed that the participants had high perceptions concerning perceived relative advantages (M = 4.2), result demonstrability (M = 3.8), and compatibility (M = 3.9) of using tablet devices for teaching purposes. Whereas the participants had neutral perceptions (mean score range between 2.9 to 3.2) concerning the voluntariness, image, ease of use, trialability, and visibility in the use of tablet devices. Regarding the impact of teacher characteristics on their perceptions of using tablet devices, the multiple regression results showed that only two characteristics of male special education teachers significantly impacted their perceptions. The first characteristic was school level at which the teachers taught, which was significantly related to the participants' perceived voluntariness (p = .03) and trialability ( p = .01). The second characteristic, teachers' years of experience, was significantly related to perceived image (p = .04) and compatibility (p = .04). Once phase one was analyzed, a qualitative case study was carried out to provide further explanations of characteristics found to significantly impact the participants' perceptions about the use of tablet devices and their applications for teaching purposes. Furthermore, this phase aimed to explore difficulties hindering the adoption of tablet devices for teaching purposes as well as the roles of school administration in adopting tablet devices. In this phase six participants were selected purposefully based on their age, years of teaching experience, specialty in teaching students with disabilities, and school level at which they taught. A semi-structured interview method was used to collect data. The collected data were analyzed using a coding approach. The results from the data analysis showed that participants' years of teaching experience and school level were critical, if not vital, when it came to the use of tablet devices as an assistive technology tool in special education classrooms. These results also supported the findings that emerged in phase one. The results from the follow-up interviews showed that four major obstacles hindered the adoption of tablet devices in the Saudi special education system. These obstacles are lack of training, class management, shortage of tablet applications in Arabic, and the process being time consuming. Also the results indicated that school leadership did not support the use of tablet devices for teaching purposes due to lack of awareness and funding. Discussions, implications, limitations of this study as well as recommendations for future research are discussed in depth in Chapter 5.Ph.D. (Doctor of Philosophy

Mobile Learning in Higher Education: A Systematic Literature Review

Mobile learning (M-Learning) has become a popular and effective method of education that leverages the ubiquity of mobile devices. M-Learning has digitally transformed the process of teaching and learning. It has tremendous potential to empower all sections of society through education and training. This study presents a systematic literature review of M-Learning. The articles were retrieved from Scopus and Web of Science databases. After applying inclusion and exclusion criteria, a final selection of 161 articles published between 2016 and 2022 was included in the review. To analyze the articles, the researchers employed the TCCM (Theory, Context, Characteristics, Methods) framework, which facilitated addressing the research questions. This review identified various theories, such as behaviorism, constructivism, cognitivism, situated learning, problem-based learning, context awareness learning, socio-cultural theory, collaborative learning, conversational learning, lifelong learning, informal learning, activity theory, connectivism, navigation, and location-based learning, that are used to support and guide the implementation of M-Learning. In terms of context, developing countries contributed to 70.8% of the studies, while developed countries contributed to 29.1%. Further, a majority of the studies, 93%, involved students followed by faculty members and only two studies involved staff from higher education management. A total of 19 unique characteristic factors have been identified, such as personal, intention, attitude, usage, utility, ease of use, learnability, social, technological, pedagogical, anxiety, enjoyment, accessibility, knowledge, experience, trust, price, and habit. A quantitative research design was used in 90% of the studies, followed by mixed methods research design in 7% of the studies, and qualitative research design in only 3% of the studies. Further, this article synthesizes previous research findings and highlights gaps for future research. Overall, this review contributes to the understanding and advancement of M-Learning as a valuable educational platform

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes

Analyzing Critical Success Factors for Sustainable Cloud-Based Mobile Learning (CBML) in Crisp and Fuzzy Environment

Mobile Learning (M-Learning), driven by technological digital advancement, is one of the essential formats of online learning, providing flexibility to learners. Cloud-based mobile learning (CBML) provides value additions by providing an economic alternative to E-learning. Revolutionary changes in smartphone design and features have enhanced the user experience, thus encouraging mobile learning. During the COVID-19 pandemic, E-Learning and M-Learning allowed continuing education to occur. These methods continue to offer more opportunities to learners than constrained face-to-face classroom learning. There are many main critical success factors (CSFs) and subfactors that play an influential role in sustainable M-Learning success. The current study focuses on the assessment and ranking of various main factors and subfactors of CBML. Analytic hierarchy process-group decision-making (AHP-GDM)- and fuzzy analytic hierarchy process (FAHP)-based methodologies were used to evaluate and model the main factors and subfactors of CBML in crisp and fuzzy environments. Higher education institutes must strive to address these main factors and subfactors if they are to fulfill their vision and mission in the teaching–learning system while adopting sustainable M-Learning

Evaluating and Prioritizing Barriers for Sustainable E-Learning Using Analytic Hierarchy Process-Group Decision Making

E-Learning is a popular computer-based teaching–learning system that has been rapidly gaining global attention during and post COVID-19. The leaping changes in digital technology have enabled E-Learning to become more effective in recent years. It offers freedom from restrictions caused by geographical boundaries and provides time flexibility in the teaching–learning process. Apart from its numerous advantages, the success of E-Learning depends upon many critical success factors (CSFs) and barriers. If the barriers that lie in the way of successful E-Learning implementation are not addressed diligently, it will limit E-Learning success. It has been revealed through past research that these barriers are serious threats that need immediate attention in their redressal. This paper attempts to reveal sixteen barriers under four different dimensions by going through a comprehensive review of the literature and engaging decision makers. Furthermore, it uses the Analytic Hierarchy Process-Group Decision Making (AHP-GDM) methodology to evaluate and prioritize them. The results obtained show that barriers related to the Institutional Management Dimension (BIMD), Infrastructure and Technological Dimension (BITD), Student Dimension (BSD), and Instructor Dimension (BID) pose the greatest challenges in the successful implementation of E-Learning. The AHP-GDM methodologies reveal the comparative relationship among these barriers as BIMD > BITD > BSD > BID and further quantify their negative effects as 46.35%, 29.88%, 12.30%, and 11.4%, respectively, on successful E-Learning systems (‘>’ indicates comparative challenges)