Today, primary school teachers face challenges when dealing with digital natives. As a result of the explosion and rapid growth in information technologies that can be used in education, there are increasing demands to adopt technology in education, in order to influence students to learn actively and motivate them to gain an effective learning process. Augmented reality applications show good potential in giving students more active, effective and meaningful learning processes. Moreover, augmented reality attracts research attention for its ability to allow students to be immersed in realistic experiences. Therefore, this study reviews the main benefits of using augmented reality applications in education. It also aims to examine user acceptance of augmented reality applications within an e-learning environment in primary schools, from the teachers’ perspective, as an initial experiment. Moreover, it explores the main barriers and benefits when adopting this technology.

Information quality frameworks are developed to measure the quality of information systems, generally from the designers’ viewpoint. The recent proliferation of e-services and e-learning particularly raises the need for a new quality framework in the context of e-learning systems. This paper proposes a new information quality framework, with 14 information quality attributes grouped in three quality dimensions: intrinsic, contextual representation and accessibility. This framework could be useful to e-learning systems designers, providers and users as it provides a comprehensive indication of the quality of information in such systems. We report results based on original questionnaire data and factor analysis supporting our conclusions.

With the prevalent use of e-learning systems as an information exchange means, the need for quality published materials within these systems has become vital. In previous work by the authors, an information quality framework for e-learning systems was developed. The proposed framework consisted of 14 quality attributes grouped in three quality factors: intrinsic, contextual representation and accessibility. Within the proposed quality framework, the authors assigned a relative importance weight for each attribute within the main quality factors, and a relative importance weight for each factor in the overall quality score. The idea of using relative importance as a parameter for the measurement is important, since it provides the framework with the flexibility to be adopted and used in different e-learning environments and with different users. The importance weights could be updated if a quality attribute or factor appeared to have the same significant value for the quality assessment no longer. This study examines whether any significant gender and cultural related differences exist in user perceptions of the relative importance of the main quality factors within the proposed framework. Reported results are based on the collection of original questionnaire data and t-test analysis supporting the conclusions.

Aim/Purpose: To encourage students’ engagement in peer assessments and provide students with better-quality feedback, this paper describes a technique for author-reviewer matching in peer assessment systems – a Balanced Allocation algorithm. Background: Peer assessment concerns evaluating the work of colleagues and providing feedback on their work. This process is widely applied as a learning method to involve students in the progress of their learning. However, as students have different ability levels, the efficacy of the peer feedback differs from case to case. Thus, peer assessment may not provide satisfactory results for students. In order to mitigate this issue, this paper explains and evaluates an algorithm that matches the author to a set of reviewers. The technique matches authors and reviewers based on how difficult the authors perceived the assignment to be, and the algorithm then matches the selected author to a group of reviewers who may meet the author’s needs in regard to the selected assignment. Methodology: This study used the Multiple Criteria Decision-Making methodology (MCDM) to determine a set of reviewers from among the many available options. The weighted sum method was used because the data that have been collected in user profiles are expressed in the same unit. This study produced an experimental result, examining the algorithm with a real collected dataset and mock-up dataset. In total, there were 240 students in the real dataset, and it contained self-assessment scores, peer scores, and instructor scores for the same assignment. The mock-up dataset created 1000 records for self-assessment scores. The algorithm was evaluated using focus group discussions with 29 programming students and interviews with seven programming instructors. Contribution: This paper contributes to the field in the following two ways. First, an algorithm using a MCDM methodology was proposed to match authors and reviewers in order to facilitate the peer assessment process. In addition, the algorithm used self-assessment as an initial data source to match users, rather than randomly creating reviewer – author pairs. Findings: The findings show the accurate results of the algorithm in matching three reviewers for each author. Furthermore, the algorithm was evaluated based on students’ and instructors’ perspectives. The results are very promising, as they depict a high level of satisfaction for the Balanced Allocation algorithm. Recommendations for Practitioners: We recommend instructors to consider using the Balanced Allocation algorithm to match students in peer assessments, and consequently to benefit from personalizing peer assessment based on students' needs. Recommendation for Researchers: Several MCDM methods could be expanded upon, such as the analytic hierarchy process (AHP) if different attributes are collected, or the artificial neural network (ANN) if fuzzy data is available in the user profile. Each method is suitable for special cases depending on the data available for decision-making. Impact on Society: Suitable pairing in peer assessment would increase the credibility of the peer assessment process and encourage students’ engagement in peer assessments. Future Research: The Balanced Allocation algorithm could be applied using a single group, and a peer assessment with random matching with another group may also be conducted, followed by performing a t-test to determine the impact of matching on students’ performances in the peer assessment activity.