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This review paper presents a systematic literature review on the use of Augmented Reality (AR) in engineering education, and specifically in student's spatial ability training, for the last decade. Researchers have explored the benefits of AR, and its application has been of increasing interest in all levels of education. Engineering students tend to have difficulties in acquiring visualization skills, and hence, AR is gaining momentum in enhancing students' learning achievements. This paper aims to present valuable information to researchers, tutors and software developers of learning technology systems concerning the advantages and limitations of AR in spatial ability training, the incorporation of adaptivity and personalization in AR applications as well as the aspects of spatial ability having been evaluated using AR and the prevalent evaluation methods for AR applications. To this direction, a total of thirty-two (32) studies were reviewed, having been published since 2010. The findings reveal an increase in the number of studies during the last three years. One major conclusion is the improvement of learners' spatial ability using AR in educational settings, and the noted challenge is the need for more learning content. One research gap that has been identified is the lack of personalization in the developed applications, offering space for future research. Concluding, this area is under-researched, and thus, there is scope for a lot of improvement.

Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework for providing recommendations of collaborative activities to learners by employing an Artificial Neural Network (ANN) and the Weighted Sum Model (WSM). In our approach, the Gardner and Korth framework is employed to identify the dimensions of the learners' collaborative learning styles. For each dimension, different weights are assigned and used as input data for the ANN. Then, the WSM and the activation function determine the output vector by mapping the resulting values. The final output of the ANN is a different percentage of collaborative activities in which learners can participate. This output is used to recommend the activities to the learners. As a testbed for our research, we have developed an intelligent tutoring system for the programming language "Java". Our system was evaluated using an established framework and the statistical hypothesis test. The results show a high degree of pedagogical affordance and usefulness of our approach as well as its positive impact on learning.

Working in groups is an important collaboration activity in the educational context, where a variety of factors can influence the prediction of the teams' performance. In the pertinent bibliography, several machine learning models are available for delivering predictions. In this sense, the main goal of the current research is to assess 28 different machine learning models, including a Deep Neural Network (DNN) which is structured by 4 hidden layers, for predicting teams' performance. Additionally, both data analysis and optimization of input data are also explored for their effectiveness in the improvement of the models' performance. One key finding of the present study is that the XGBoost model succeeded better prediction results, and its precision and robustness were found to be higher, compared to the other models. Additionally, data optimization was shown to be an essential procedure, since the prediction accuracy of all the models, and specifically, that of the XGBoost, improved and found to be 96% during the first phase that of the process, and 94% during the second phase that of the product. Similarly, after applying the hyperparameter tuning and data optimization, the prediction accuracy of the DNN was also improved and found to be 89.94% and 86.16%, during the same two phases. Finally, for interpreting the output of the ML models, in terms of features' importance, the Shapley Additive Explanations framework (SHAP) was used.

The closure of educational institutions due to the COVID-19 pandemic leads imperatively to the utilization of technological advances and the Internet for enabling the continuity of learning. To this direction, Mobile Game-based Learning (MGbL) can be beneficial to teaching and learning; since, from technological perspective, most students prefer to use their mobile devices, such as smartphones or tablets, and from pedagogical perspective, incorporating gaming in educational process can boost students' motivation for learning and improve their learning outcomes. Hence, this study investigates learners' intention to use MGbL as an alternative educational practice during the COVID-19 pandemic, by modeling the pedagogical affordance of this technology and student interactions with it. As a testbed for this research, a MGbL application was used for the instruction of the programming language C# in higher education, during the lockdown period of 2020. The findings reveal that the MGbL technology has a significant and positive impact on student engagement and academic performance.

The integration of Augmented Reality (AR) in welding training is considered to increase the efficiency, security and time gain in operations, reducing consumable and infrastructures costs. Prior research has examined the integration of AR-simulation in applications, like medical operations or aviation, showing the need for greater usability of these systems. However, research on AR integration in welding training is yet limited. It can help new welders learn effectively and be prepared to work in the industry. The motivation of this study was the ongoing use of AR in manufacturing training and its novelty is the analysis of the most significant factors affecting the actual AR system use. The main objective of this research is to evaluate the use of AR technology in welding training using an innovative model, appropriately extended to consider pedagogy and technology. The contribution of this paper is the exploration and understanding of the factors associated with the AR welding training which can lead to goal fulfillment and, subsequently, impact users' choice. This study is based on a modified Technology Acceptance Model, extending it by two external variables (perceived enjoyment and system quality) and the sample is 200 trainees. The findings show that the external variables are predictors of perceived usefulness and ease of use. The intention to use AR simulator is positively influenced directly by system quality and perceived ease of use. The findings help AR developers enhance the quality of AR-simulation training systems to enhance users' experience and their behavioral intention to use them.

This paper addresses the interesting issue of mobile-assisted language learning using novel techniques for further improving the adaptivity and personalisation to students. The domain model of the system includes English and French language concepts, and its user model holds information about students and their progress. It also embodies a database of categories of errors and misconceptions which have been reported as common in the related literature. The system is also responsible for conducting model-based error diagnosis using machine learning techniques and identifying errors such as knowledge transfer, spelling or verb mistakes. In conjunction with error diagnosis, the system employs fuzzy logic to automatically model these misconceptions and errors and then provide personalised feedback to students based on their personal learning needs. The system has been fully evaluated, using the CIAO! framework and t-test. The evaluation results are positive and encouraging regarding the educational effectiveness.

Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students' cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students' knowledge level to fuzzy weights, and using rule-based decision making, delivers adequate learning activities regarding their kind and complexity degree. Background: Tutoring through adaptive learning activities can be a powerful tool to support learners in practical courses, like computer programming. However, published results from pertinent literature are not oriented to the adaptivity of the domain knowledge in terms of the volume, kind, and complexity of the learning activities delivered to students. There is scope for a lot of improvement to this direction. Intended Outcomes: Combining learning theories with adaptive tutoring enhances student-centered learning, promotes student engagement, and improves knowledge acquisition. Application Design: An adaptive tutoring system was developed for supporting undergraduate students in the C# programming language course for an academic semester. The system delivers adaptive learning activities to students' cognitive skills using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of a RBT for designing the learning material. Findings: At the end of the academic semester, students' data have been collected and a detailed evaluation was conducted. The results showed that the presented approach outperforms others which lack adaptivity in domain knowledge and learning theories, improving significantly the students' learning outcomes.