to many, however, it constantly appears as an instructional problem. The final two chapters serve as excellent summary statements. Chapter 13 emphasizes the application of previous text material to the classroom situation. Of great significance is the author's discussion of commonly defined independent, dependent, and moderator variables. The final chapter focuses on evaluation in the overall sense, particularly as it relates to programs of study. The principles of measurement and research design are applied to the evaluation process through several ex-

Educational Research: Competencies for Analysis and Application:

Flip your classroom: Reach every student in every class every day

During the COVID-19 pandemic, educational institutions were shut down all over the world, which impacted over 60% of students and caused a massive disruption of the education system. The goal of this paper was to identify the critical success factors for E-learning during COVID-19 using the multi-criteria Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques to enhance the educational process. Data were generated by interviewing 69 E-learning managers in educational institutions during COVID-19 based on defined evaluation criteria and E-learning approaches through several channels. We found that technology management, support from management, increased student awareness to use E-learning systems, and demanding a high level of information technology from instructors, students, and universities were the most influential factors for E-learning during COVID-19. Among the five learning systems, blended learning was the most suitable learning system to practice. These results demonstrated that, regardless of how extraordinary the technology is in an educational institution, the readiness of E-learning execution played a large role in boosting the educational process during the COVID-19 pandemic.

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E-learning critical success factors during the covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives

Google classroom can work in unidirectional process as it can serve the teachers’ strategies and styles on one hand and students’ perception, understanding, and effective participation in different classroom skills on the other hand. The ac-ceptance of Google classroom is affected by different factors. Some of them are still not clearly specified and discussed in previous research; therefore, they need further investigation. Based on the previous assumption, this study is an attempt to examine the factors that affect the students’ acceptance of Google classroom at Al Buraimi University College (BUC) in Oman. The Technology Acceptance Model (TAM) was adopted to formulate the hypotheses of the current study. The data was collected through an online questionnaire with 337 respondents. The Partial Least Square-Structural Equation Model (PLS-SEM) approach was used to assess both the measurement and structural models. The results of the study prove that both the perceived ease of use (PEOU) and perceived usefulness (PU) positively influence the behavioral intention, which in turn influence the actual usage of Google classrooms. This study helps the decision makers of the higher educational institutions to have a better understanding of the effectiveness of us-ing Google classroom by their students. It is assumed that it helps in measuring the level of students’ acceptance to the previously mentioned technology.

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Students Acceptance of Google Classroom: An Exploratory Study using PLS-SEM Approach

Knowledge sharing is an activity through which knowledge, namely, information, skills, or expertise, is exchanged between people, friends, families, communities or organizations. Online knowledge sharing activities are flourishing with the advent of social media and digital life. However, despite of the importance of the online knowledge sharing methods and mechanisms, there is not any comprehensive and systematic study about studying and analyzing its important techniques. Therefore, the main aim of this paper, the comprehensive, detailed, and systematic study, and survey of the state-of-the-art knowledge sharing mechanisms in an online environments is provided. Also, this paper presents a systematic literature review (SLR) on the online knowledge sharing literatures up to end of 2015. We identified 348 papers, which are reduced to 251 primary studies through our paper selection process. Also, the broad overview of the literature provides insights into potential areas for further research. By providing state-of-the-art information, this survey will directly support academics and practicing professionals in their understanding of developments in online knowledge sharing mechanisms and techniques.

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Online knowledge sharing mechanisms: a systematic review of the state of the art literature and recommendations for future research

Learning activities in the computer lab is one of the challenging in higher education. Subject that is most practical activities such as Data Mining are by nature illustrative or demonstrative in the computer lab that emphasize the acquisition of observational skills; and allow students to see the concept dealt in action and relate theory more closely to reality. However, the students’ reaction to practical work is often negative as a result they are not effective in laboratory work and this may reflect a student perception that there is lack of clear purpose for the lab hands on task. The main objective of this study is to explore the effectiveness of Google Classroom’s active learning activities for data mining subject under the Decision Sciences program. A set of questionnaire has been distributed to a sample of 100 students who enrolled data mining subject were used in this study. The analysis of the data was carried out using Technology Acceptance Model (TAM) to examine the relationship between the identified factors and the effectiveness of the learning activities. The results prove that majority of the students satisfy with the Google Classroom’s tool that were introduced in the class where all ratios are above averages. In particular, comparative performance is good in the areas of ease of access, perceived usefulness, communication and interaction, instruction delivery and students’ satisfaction towards the Google Classroom’s learning activities.

The Application of Google Classroom as a Tool for Teaching and Learning

As the world is being developed with the new technologies, discovering and manipulating new ideas and concepts of online education are changing rapidly. In response to these changes, many states, institutions, and organizations have been working on strategic plans to implement online education. At the same time, misconceptions and myths related to the difficulty of teaching and learning online, technologies available to support online instruction, the support and compensation needed for high-quality instructors, and the needs of online students create challenges for such vision statements and planning documents. This paper provides analysis and evaluation of the effectiveness of Google Classroom’s active learning activities for data mining subject under the Decision Sciences program. Technology Acceptance Model (TAM) has been employed to measure the effectiveness of the learning activities. A total of 100 valid unduplicated responses from students who enrolled data mining subject were used in this study. The results indicated that majority of the students satisfy with the Google Classroom’s tool that were introduced in the class. Results of data analyzed showed that all ratios are above averages. In particular, comparative performance is good in the areas of ease of access, perceived usefulness, communication and interaction, instruction delivery and students’ satisfaction towards the Google Classroom’s active learning activities.

Google classroom as a tool for active learning

Assessing rules with interestingness measures is the pillar of successful application of association rules discovery. However, association rules discovered are normally large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this framework. The proposed strategy combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non- significant rules. Moreover, we consider real world datasets which are characterized by the uniform and non-uniform data/items distribution with a mixture of measurement levels throughout the data/items. The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole.

Interestingness measures for association rules based on statistical validity

This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

Strategic preparation of e-learning application includes decision making regarding the most suitable type of elearning on different levels. The survey has been carried out on the sample of 95 respondents consisted of administrative and academic staff, and postgraduate students in Malaysia. They were asked to assess the relative importance of five e-learning evaluation criteria to be analysed by using AHP technique. Furthermore, they also rated the performance of five identified e-learning approaches under each of the requirements. The overall performance of each e-learning approach was computed by using TOPSIS method. The results suggested that Flipped Classroom is the most suitable e-learning approach, while ‘Strategic readiness for e-learning implementation’ found to be the most important criterion. The paper is suggesting a quantitative evaluation method for decision-makers who are strategising modern technologies in higher educational settings.

Izwan Nizal Mohd Shaharanee

Papers

The Application of Google Classroom as a Tool for Teaching and Learning

Google classroom as a tool for active learning

Interestingness measures for association rules based on statistical validity

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

Evaluation of E-Learning Approaches Using AHP-TOPSIS Technique