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Numerous educational applications (APP) have been developed to assist traditional classroom teaching and student learning. APP quality plays a critical role in influencing students' learning behaviors. However, the role negative mindsets, especially computer anxiety, play in how APP quality affects student engagement remains unknown. To address the relationships among APP quality, computer anxiety and student engagement in an APP-based learning environment, we developed an extended information system (IS) success model that includes interface and instructor quality.

To empirically test the proposed model, we conducted a survey with a sample of 225 university students and examined the hypotheses using the partial least squares (PLS) method.

Computer anxiety was demonstrated to fully mediate the relationships between student engagement and interface quality and service quality and system quality. In addition, the instructor quality acts as a partial mediator of the relationship between computer anxiety and student engagement.

This study reveals the important mediating role of computer anxiety in APP-assisted learning and the special status of instructor quality and user experience in influencing student engagement. The findings of this study shed meaningful light on the practical implications for instructors and APP software developers.

The purpose of this paper is to propose a hybrid model based on the expectation-confirmation model (ECM), flow theory, and updated DeLone and McLean information system (IS) success model to examine whether quality factors as the antecedents to nurse beliefs affected nurses’ intention to continue using the blended electronic learning (e-learning) system.

Sample data for this study were collected from nurses at five hospitals in Taiwan. A total of 500 questionnaires were distributed, 396 (79.2 percent) questionnaires were returned. Consequently, 378 usable questionnaires were analyzed in this study, with a usable response rate of 75.6 percent. Collected data were analyzed using structural equation modeling.

Information quality, system quality, support service quality, and instructor quality contribute significantly to perceived usefulness (PU), confirmation, and flow, which together explain nurses’ satisfaction with the usage of the blended e-learning system, and this in turn leads to their continued system usage intention.

First, the application of the ECM with the view of updated DeLone and McLean IS success model reveals deep insights into quality evaluation (including information quality, system quality, and support service quality) in the field of nurses’ e-learning continuance intention. Especially, this study additionally contributes to the identification of instructor quality that may lead to nurses’ continued blended e-learning usage intention. Next, the empirical evidence on capturing both extrinsic motivator (i.e. PU) and intrinsic motivator (i.e. flow) for completely explaining quality antecedents of nurses’ blended e-learning continuance intention is well documented.

The lessons of quality management apply to services as well as tangible goods. Awareness also has been increasing that services, like tangible goods, can be guaranteed as a means of implementing a total quality management (TQM) orientation in the organization. While higher education has been exploring some of the tenents of TQM, it has been slow to embrace the power of service guarantees. In this conceptual article we present a system of service guarantees designed to foster a TQM orientation in higher education. We propose that institutions consider a system of guarantees aimed at three primary constituent groups – students, faculty, and employers – over the short, medium and long term. The rationale and implications of the guarantee system are explored, and possible impediments are discussed.

The main purpose of this study is to examine whether quality factors as the antecedents to learner beliefs can affect learners' intention to use an e‐learning system.

This study gathered sample data from eight high‐tech companies in Taiwan. A total of 680 questionnaires were randomly distributed, 522 questionnaires were returned for a response rate of 76.76 percent, and 483 usable questionnaires were analyzed, with a usable response rate of 71.03 percent. Data were analyzed by using structural equation modeling.

Information quality, service quality, system quality, and instructor quality, as the antecedents of e‐learning acceptance can provide detailed accounts of the key forces underpinning employees' perception with regard to their beliefs (i.e. perceived usefulness, perceived ease of use, and perceived enjoyment), and this situation can further enhance employees' usage intention of the e‐learning system.

Based on the extended technology acceptance model and the updated DeLone and McLean information systems success model, this study integrates related e‐learning quality factors including information quality, service quality, and system quality into the research model and further contributes additionally to the identification of instructor quality that may lead to e‐learning acceptance. Also, it should be noted that the empirical evidence on capturing both extrinsic and intrinsic motivators for completely explaining quality antecedents of e‐learning acceptance is well documented in this study. Hence, this study contributes significantly to the body of research on evaluating the quality antecedents of e‐learning acceptance.

On the basis of the justice, attachment, social support, self-determination theories, this research paper examines the impact of the student–instructor relationship construct on the customer-centric measures of overall student satisfaction, and perceived value for money and their impact of the behavioral intentions as an endogenous construct. We considered universities as providers of complex services focusing on students' service quality and students as the customers of the higher education institutions.

A survey instrument was used to collect data among undergraduate and graduate business students in a medium-sized Canadian university (N = 178). Partial least squares structural equation modeling was used to analyze the strength, significance, and effect sizes of the relationships between the key constructs.

The results indicate that the student–instructor relationship is significantly related to student satisfaction and value for money perceptions. Also, the student satisfaction and behavioral intentions, value for money and student satisfaction, and value for money and behavioral intentions relationship were significant.

The perceived quality of student–instructor relationship and its relationship to customer-centric measures like satisfaction, value for money and behavioral intentions has received relatively little attention in previous research and was discovered to be an important contributor to the perceived student satisfaction and value for money. The importance of the student–instructor relationship is further emphasized indirectly via the perceived value for money construct to student satisfaction.

This study aims to propose and validate a model for e-Learning success based on students’ experiences in the “new normal.” To achieve this goal, this study focused on answering three research questions: (1) What are the students’ experiential factors that impact e-Learning? (2) How do these experiential factors affect e-Learning success? (3) In what ways does a multimethod provide a comprehensive perspective and an in-depth understanding of students’ e-Learning experiences in the new normal?

This study applied a mixed-methods sequential approach using exploratory, confirmatory and complementary studies. First, this study undertook a text-mining exploratory analysis of the review data to extract e-Learning topics. Then, based on the Information Systems (IS) success model, this study identified an integrated framework drawn from the results of the text-mining analysis. Second, this study proposed an e-Learning, experience-based success model and corresponding hypotheses and conducted a confirmatory study with surveys to validate the model. Third, this study conducted in-depth interviews to better identify the phenomenon of interest.

The five factors extracted from the first stage are system quality, lecture content, teaching quality, online interaction and achievement. This study subsequently confirmed the significant relationships between the e-Learning success factors in the second stage based on the IS success model. Finally, a complementary study identified the importance of interactivity for e-Learning success in the new normal.

To the best of the authors’ knowledge, this paper is the first to develop an e-Learning success model using a comprehensive mixed-methods approach.

Evaluation is the process by which we estimate how things should go, explore how things are going, and determine how things went in terms of course redesign. In this chapter, we examine formative and summative methods for assessing student learning and establishing teacher effectiveness and course quality. Evaluation is a subjective, value-laden process. To introduce the rigor needed to make it meaningful, evaluation should be multifaceted, planned in advance, made transparent to learners, and employ valid and reliable methods. Moving courses online presents both opportunities and challenges for evaluation. We explore ways to implement assessment to make full use of the advantages of technology while mitigating the problems associated with online delivery.

The purpose of this paper was to examine the perceived outcomes of e-learning by identifying key attributes affecting user (learner) satisfaction in higher education institutes.

A conceptual model considering user satisfaction as a key construct was developed through critical literature review and expert opinion. The model is empirically validated using confirmatory factor analysis and structural equation model in the context of higher education institutions. A sample of 802 users comprising of engineering and management students has been used for the analysis.

Course attributes, system attributes, interactive attributes and instructor attributes were found to have an influence significantly on user satisfaction. Instructor attributes were the topmost significant contributor followed by the course attributes.

Delivery of educational programs through e-learning platforms has increasingly gained traction throughout the world owing to its locational, time and convenience-related facets. Further, the ongoing global pandemic has catalysed acceptance of e-learning platforms thus attracting large number of learners and teachers for facilitating the teaching-learning process. This paper is a novel attempt to identify the existing gaps in teaching-learning process in the context of e-learning.

This study is original and provides new insights into how e-learning platforms and higher education institutions can ensure higher user satisfaction and learning in current challenging times. This paper will also be of interest to policymakers.

The purpose of this study is to propose the research model integrating the expectation-confirmation model with the views of learning engagement (LE) and extending DeLone and McLean information systems (IS) success model to examine whether quality determinants as antecedents to students' beliefs can influence students' continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed, and 363 (60.5%) useable questionnaires were analyzed using structural equation modeling in this study.

This study proved that students' perceived knowledge quality, system quality, interface design quality, learner–instructor interaction quality, and collaboration quality all positively caused students' perceived usefulness, confirmation and LE in MOOCs, which jointly explained students' satisfaction with MOOCs and subsequently resulted in students' continuance intention of MOOCs.

This study fully evaluates IS-related and interaction-related quality determinants via an understanding of students' state of LE in explaining students' continuance intention of MOOCs that is difficult to expound with only their utilitarian perception of MOOCs. Hence, this study contributes to deep insights into an all-round quality evaluation in the field of MOOCs continuance intention and takes extrinsic and intrinsic motivators into account in the theoretical development of MOOCs continuance intention to acquire a more comprehensive and robust analysis.

The aim of this paper is to present conclusions from a series of studies on the introduction of new learning technologies at the College of Judea and Samaria, Israel, including course websites and a computerized marking system.

This paper summarizes four distinct empirical studies based on annual student surveys, questionnaires developed specifically for the study and the College exam database. The first series of three studies was designed to examine usage rates and students' evaluations of effectiveness of website courses, in three concentric circles in the College: on the institutional, faculty and departmental level. The fourth study tracked an institutional initiative towards enhanced quality of instruction, by comparing College‐wide data on students' ratings and grades for courses whose instructors implemented and used a computerized grading system for multiple‐choice on‐line exams.

Studies show that academic e‐learning in Israel has focused on quantity rather than quality; and on superficial technological adoption rather than conceptual pedagogical change process.

Institutions wishing to encourage the introduction of innovative learning aids and technologies must develop the necessary conditions that ensure that technology adoption serves rather than dominates pedagogy.

This paper discusses the reasons for a focus on quantity rather than quality, and on superficial technological adoption rather than the introduction of a conceptual pedagogical change process, in Israeli higher education's approach to online learning. Empirical findings highlight the need to introduce technological innovations that are grounded in and develop from a sound pedagogical base.