Guest Editorial: Special Issue on Emerging Trends in Education - Part I

The first eight papers accepted for the Special Issue on “Emerging Trends in Education” were published previously [1]. Here, we introduces the remaining eight papers, which cover quite a varied set of topics, encompassing online education, adaptive learning, student modeling and analytics, as well as gamification. For example, in “FORGE toolkit: leveraging distributed systems in eLearning platforms” Guillaume Jourjon et al. explore technologies supporting online education. The focus is on the creation of a distributed architecture meant to support large-scale laboratory activities by leveraging experimentation facilities deployed in international initiatives for the development of e-learning resources. In particular, the authors present the design of an ecosystem based on configurable widgets that can be exploited in the e-book authoring process to enable the integration of tools designed for producing teaching and educational materials with services offered by experimentation facilities. The authors tested the proposed framework in the context of network and communication courses offered by both academic and industrial institutions worldwide. In “Tutorials in eLearning: How presentation affects outcomes”, Leana Copeland and Tom Gedeon present a possible application of an emerging human-computer interaction technology like eye tracking in the context of education. More specifically, their goal is to study how different sequences of text and test questions could influence performance in terms of understanding and, ultimately, of learning effectiveness for English language readers. According to the authors, results obtained could be exploited to guide the design of learning materials capable to adapt to users’ behavior. In “Open student models of core competencies at the curriculum level: Using learning analytics for student reflection” Chih-Yueh Chou et al. analyzed the quantity and quality of courses and associated grades to estimate the proficiency level of core competences. Course-competency diagnostic tools, course work performance radar charts and peer-based ranking tables are proposed as a means to enable student reflection by promoting awareness, selfassessment, learning autonomy, planning and positive group interactions. In “Identifying at-risk students for early interventions: A time-series clustering approach” by Jui-Long Hung et al., the authors apply a clustering approach over disaggregated time series data from the logs of a LMS regarding frequency of course material access, frequency of forum reads, number of discussions posted, number of replies posted, demographic information, and student grades to generate more accurate predictions about at-risk students. Experimental results demonstrate that, compared to alternative methods working on aggregate data, the proposed approach supports earlier detection, which allows online instructors to develop suitable interventions via course design or student-teacher interactions. In “Similarity-based grouping to support teachers on collaborative activities in exploratory learning environments” by Sergio Gutierrez-Santosa et al., the focus is shifted primarily on teachers. The authors propose a tool that is meant to help teachers to cope with pragmatic and logistic constraints that are typical of collaborative learning scenarios in a physical class context. The tool supports the formation of groups capable of enacting a profitable learning experience where students engage in productive discussions and reflect on their approaches to a given problem by justifying and critiquing proposed solutions. The designed tool has been tested with young students working with a mathematical learning system designed to develop their algebraic ways of thinking by identifying relationships in figural patterns. The tool first compares approaches towards the solution, in order to identify complementary students. Then, it forms pairs of students by using a heuristic aimed to minimize similarity between approaches for the whole class. Experimental tests showed that the proposed approach can reduce the time needed for performing the allocation, by producing groups that are in line with teachers’ own expectation of good pairing. Related to the topic of group formation, is the relationship between group structure and educational effectiveness. This is explored in “Relation between combination of personal characteristic types and educational effectiveness for controlled project-based learning” by Yusuke Sunaga et al., where a study of the impact of students’ personalities on the knowledge and skills acquired when working on a learning Digital Object Identifier 10.1109/TETC.2017.2667298