No abstract

Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education. Thus, this study aims to conduct a systematic review on big data in education in order to explore the trends, classify the research themes, and highlight the limitations and provide possible future directions in the domain. Following a systematic review procedure, 40 primary studies published from 2014 to 2019 were utilized and related information extracted. The findings showed that there is an increase in the number of studies that address big data in education during the last 2 years. It has been found that the current studies covered four main research themes under big data in education, mainly, learner’s behavior and performance, modelling and educational data warehouse, improvement in the educational system, and integration of big data into the curriculum. Most of the big data educational researches have focused on learner’s behavior and performances. Moreover, this study highlights research limitations and portrays the future directions. This study provides a guideline for future studies and highlights new insights and directions for the successful utilization of big data in education.

Big Data Adoption (BDA) has already gained tremendous attention from executives in various fields. However, it is still not well explored in the education sector, where a large amount of academic data is being produced. Therefore, integrating Technology Organization Environment (TOE) and Diffusion of Innovation (DOI), this study aims to develop a theoretical model to identify the factors that influence BDA in the higher education sector. To do so, significant technology-, organization-, and environment-related factors have been extracted from previous BDA studies. Meanwhile, the moderating effects of the university size and the university age are added into the developed model. A sample of 195 data was collected from the managerial side of virtual university (VU) campuses in Pakistan using an online survey questionnaire. Structural equation modeling (SEM) was used to test the research model and developed hypotheses. The results showed that relative advantage, complexity, compatibility, top management support, financial resources, human expertise and skills, competitive pressure, security and privacy, and government policies are significant determinants of BDA. However, the results did not support the influence of IT infrastructure on BDA. Based on the findings, this study provides guidelines for the successful adoption of big data in higher education sector. This study can serve as a piece of help to the ministry of education, administrators, and big data service providers for the smooth adoption of big data.

In recent years, e-learning has become pivotal in higher education sectors. Researchers are correlating novel approaches with e-learning to facilitate education. However, despite the increase in e-learning research, there is still a lack of comprehensive literature analysis of e-learning in the higher education sector. Thus, this study aims to conduct a systematic literature review of the literature on e-learning in higher education. This study classifies the selected studies according to the focus of the study, utilizes a theoretical model and framework, and research methods. Also, it presents limitations and future research directions of e-learning in the higher education sector. A systematic approach is conducted, and a total of 47 relevant articles published between the year 2011 and 2019 were selected based on the inclusion and exclusion criteria. The findings on selected studies focus on the adoption, acceptance, readiness, and user insight, as well as e-learning expansion and challenges in the higher education sector. This study also classified theoretical models and frameworks based on their usage in the pre-adoption, adoption, and post-adoption stages. The findings revealed that most of the theoretical models and frameworks were used at the post-adoption stage. Nevertheless, this study revealed that most of the current studies in this domain were conducted using a quantitative research approach. Finally, this study highlighted limitations and presented possible future research directions as a guide for further enhancement in e-learning and higher education studies.

An "inverted classroom" is a teaching environment that integrates the use of technology with active learning by exploration. In such a teaching environment, the class contact time is used for learning by doing whereas passive teaching is done out of class through the use of some communication medium (video lectures, or web-based content). The aim of this study is to investigate the inverted class room pedagogy in teaching undergraduate computer science course. Its primary objective is to help in bridging the gap of knowledge pertaining to use of technology and its effectiveness within the context of undergraduate computer science education. The course selected for this study is that of Data Structures offered at undergraduate level at a Pakistani university. This study employs the use of the Quasi-Experiment, two groups (Control and Treatment) -Pre-test and Post-test design model of experimentation. Key deductions of this study are that use of inverted class room pedagogy has positive impact on the learning outcomes. Students, who were participants of this experiment, showed a higher knowledge gain than their control group counterparts. This difference is especially pronounced in the areas of problem solving and programming ability. The results from this study can be used to transform other traditional computer science courses to inverted form. Inverted class room pedagogy shows the potential to address issues of student disinterest, lack of motivation and lagging attendance.