Various review studies were conducted to provide valuable insights into the current research trend of the Technology Acceptance Model (TAM). Nevertheless, this issue still needs to be investigated from further directions. It has been noticed that research overlooks the investigation of TAM with regard to Mobile learning (M-learning) studies from the standpoint of different perspectives. The present study systematically reviews and synthesizes the TAM studies related to M-learning aiming to provide a comprehensive analysis of 87 research articles from 2006 to 2018. The main findings include that most of the TAM studies involving M-learning focused on extending the TAM with external variables, followed by the studies that extended the model by factors from other theories/models. In addition, the main research problem that was frequently tackled among all the analyzed studies was to examine the acceptance of M-learning among students. Moreover, questionnaire surveys were the primarily relied research methods for data collection. Additionally, most of the analyzed studies were undertaken in Taiwan, this is followed by Spain, China, and Malaysia, respectively among the other countries. Besides, most of the analyzed studies were frequently conducted in humanities and educational context, followed by IT and computer science context, respectively among the other contexts. Most of the analyzed studies were carried out in the higher educational settings. To that end, the findings of this review study provide an insight into the current trend of TAM research involving M-learning studies and form an essential reference for scholars in the M-learning context.

Technology Acceptance Model in M-learning context: A systematic review

The introduction of Artificial Intelligence technology enables the integration of Chatbot systems into various aspects of education. This technology is increasingly being used for educational purposes. Chatbot technology has the potential to provide quick and personalised services to everyone in the sector, including institutional employees and students. This paper presents a systematic review of previous studies on the use of Chatbots in education. A systematic review approach was used to analyse 53 articles from recognised digital databases. The review results provide a comprehensive understanding of prior research related to the use of Chatbots in education, including information on existing studies, benefits, and challenges, as well as future research areas on the implementation of Chatbot technology in the field of education. The implications of the findings were discussed, and suggestions were made.

Chatbots applications in education: A systematic review

This study examined the utilisation of learning analytics to support study success in higher education. The main research question was to identify whether there is a link between learning analytics and the respective intervention measures to increase study success at higher education institutions. The systematic review included empirical studies conducted during the past five years. Search terms identified 6,220 articles from various scientific sources. After duplicated articles were removed, there were 3,163 articles remaining. Each of the articles were screened and the inclusion criteria (e.g., peer-reviewed, rigorous research findings) limited the key studies to 41 articles. This paper presents an overview of the results of this systematic review. It is concluded that evidence can be found supporting the use of learning analytics to support study success in higher education. However, study success may not be exclusively the result of the use of learning analytics but also some additional means of technological or institutional support. The findings also suggest a wider adoption of learning analytics systems as well as work towards standardisation of learning analytics procedures which can be integrated into existing digital learning environments.

https://link.springer.com/content/pdf/10.1007/s11423-020-09788-z.pdf

Utilising Learning Analytics to Support Study Success in Higher Education: A Systematic Review.

/pdf/what-computational-thinking-is-39dboi4emr.pdf

What Computational Thinking Is

The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario.

/pdf/big-educational-data-analytics-survey-architecture-and-tqz2g47k3j.pdf

Big Educational Data & Analytics: Survey, Architecture and Challenges

Tutoring In Project-Based Learning

The work described in this paper relates to the formal description, implementation and use of a web-based personalized assessment system. The assessment techniques are formalized with First Order Logic rules which are able to reason over resources annotated with Semantic Web metadata formats according to LOM and IMS/ QTI standards. They were tested using TRIPLE, a rule-based language for the Semantic Web. We also describe the service-based architecture, which was implemented as part of the Personal Reader Framework. Personalization functionalities are available as web services that communicate via Semantic Web technologies without the need for centralized control. Appropriate assessment resources are searched after checking the learner's last saved performance. We illustrate this system with an object oriented programming course taken by students in our university.

Assessment personalization in the Semantic Web

In this paper we present a flexible assessment framework for eLearning based on semantic web technologies. The assessment techniques are formalized as reasoning rules. These rules are able to reason over resources annotated with semantic web metadata formats and according to the standards LOM and IMS QTI. The framework is tested using TRIPLE, a rule-based language for the semantic web.

An Assessment Framework for eLearning in the Semantic Web

An important concern in web based educational environment is the task of designing and preparing learner assessment, which would enhance the learning process. Currently the majority of Computer Assisted Assessment tools provide an assessment of the learners, without an analyisis of the adaptive content presentation. In this paper we propose a logic-based formalism to characterize adaptive content presentation according to learner assessment. Recent researches in Adaptive Educational Hypermedia Systems can be effectively used to specify an assessment framework able to present parts of online courses according to an accurate evaluation of the learner's current knowledge level. Course implementation through the standards IEEE LOM and IMS QTI permits the organization of the learning and the assessment content in units, offering multiple views on the material, by a separation of the content and its description and putting in place a self evaluation mechanism.

A Generic Framework for Assessment in Adaptive Educational Hypermedia.

With the increasing development of mobile technologies, the learning environment is currently undergoing a major shift. Access to contextual information in a mobile learning environment aims to meet the needs of learning and assessment personalization according to various learners' profiles and a range of learning contexts. Semantic Web technologies have been applied in recent years with different purposes in education. But, their applications for generating useful personalized mobile assessment resources have not been researched enough so far. In this paper, an approach making use of semantic Web technologies to support personalized self-assessment in mobile environments is described. Assessment techniques are formalized with First Order Logic rules which allow to personalize assessment activities. We also propose an algorithm for semantic assessment resources retrieval. Finally, a Mobile Semantic Web Assessment Personalization system is presented. The qualitative and quantitative evaluation of the proposed system is also provided.

/pdf/a-context-aware-approach-for-personalized-mobile-self-2xvzqgsqyz.pdf

Lilia Cheniti-Belcadhi

Papers

Tutoring In Project-Based Learning

Assessment personalization in the Semantic Web

An Assessment Framework for eLearning in the Semantic Web

A Generic Framework for Assessment in Adaptive Educational Hypermedia.

A Context-aware Approach for Personalized Mobile Self-Assessment