Deivid E. S. Silva, Tayana Conte, Natasha M. C. Valentim
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A Systematic Mapping Study about Learner Experience Design in Computational Systems
Contemporary society is characterized by diversity and intricacy, necessitating more meaningful learning experiences. To meet these evolving needs, the incorporation of computational systems into education must acknowledge the distinctive characteristics of learners. Therefore, we conducted a Systematic Mapping Study (SMS) to investigate technologies that support the Learner eXperience (LX) design in computational systems. LX refers to learners’ perceptions, reactions, and achievements while engaging with learning resources, encompassing digital games, simulations, and multimedia. The SMS results uncovered distinct LX design technologies, with a noticeable inclination towards learner-centric strategies. Interestingly, the results highlighted a scarcity of research targeting non-traditional learning environments (e.g., technical visits) and that facilitate interactions among learners beyond their own classmates (e.g., industry experts). In this way, the SMS contributes by revealing LX design technologies, LX design elements, relevant constructs/theories, computational systems, environments, contexts, and other related factors, thereby enhancing the understanding of optimal learning experiences within computational learning systems.
期刊介绍:
INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.