Review Article
Alez Lagos-Castillo, Andrés Chiappe, María-Soledad Ramirez-Montoya, Diego Fernando Becerra Rodríguez
CONT ED TECHNOLOGY, Volume 17, Issue 1, Article No: ep543
ABSTRACT
It may seem that learning platforms and systems are a tired topic for the academic community; however, with the recent advancements in artificial intelligence, they have become relevant to both current and future educational discourse. This systematic literature review explored platforms and software supporting personalized learning processes in the digital age. The review methodology followed PRISMA guidelines, searching Scopus and Web of Science databases. Results identified three main categories: artificial intelligence, platforms/software, and learning systems. Key findings indicate artificial intelligence plays a pivotal role in adaptive, personalized environments by offering individualized content, assessments, and recommendations. Online platforms integrate into blended environments to facilitate personalized learning, retention, and engagement. Learning systems promote student-centered models, highlight hybrid environments’ potential, and apply game elements for motivation. Practical implications include leveraging hybrid models, emphasizing human connections, analyzing student data, and teacher training. Future research directions involve comparative studies, motivational principles, predictive analytics, adaptive technologies, teacher professional development, cost-benefit analyses, ethical frameworks, and diverse learner impacts. Overall, the dynamic interplay between artificial intelligence, learning platforms, and learning systems offers a mosaic of opportunities for the evolution of personalized learning, emphasizing the importance of continuous exploration and refinement in this ever-evolving educational landscape.
Keywords: improving classroom teaching, data science applications in education, human-computer interface, learning communities, distributed learning environments
Research Article
Eirini Tzovla, Katerina Kedraka, Thanassis Karalis, Marina Kougiourouki, Konstantinos Lavidas
CONT ED TECHNOLOGY, Volume 13, Issue 4, Article No: ep324
ABSTRACT
Teachers’ Professional Development Massive Open Online Courses (TPD-MOOCs) are a new form of MOOCs and have influenced an intense research interest. This study reports on the design and implementation of a TPD-MOOC which utilizes digital educational content and Open Educational Recourses (OER) and supports in-service elementary school teachers to enhance their self-efficacy beliefs. In the design framework we take into consideration the findings of previous research and the educational needs of the participants. We conducted an experimental design research and compared the teachers’ self-efficacy beliefs before and after their participation in a TPD-MOOC. A total of 251 teachers enrolled in this course and 142 of them completed it. We used quantitative data to measure the enhancement of teachers’ self-efficacy beliefs and the effectiveness of the course. The results provide evidence that our TPD-MOOC improved in service elementary school teachers’ self-efficacy beliefs in teaching biological concepts. Recommendations are made for future research.
Keywords: teacher professional development, MOOC, self-efficacy beliefs, elementary education, improving classroom teaching
Research Article
Sacide Guzin Mazman Akar, Arif Altun
CONT ED TECHNOLOGY, Volume 8, Issue 3, pp. 195-213
ABSTRACT
The purpose of this study is to investigate and conceptualize the ranks of importance of social cognitive variables on university students’ computer programming performances. Spatial ability, working memory, self-efficacy, gender, prior knowledge and the universities students attend were taken as variables to be analyzed. The study has been conducted with 129 2nd year undergraduate students, who have taken Programming Languages-I course from three universities. Spatial ability has been measured through mental rotation and spatial visualization tests; working memory has been attained through the measurement of two sub-dimensions; visual-spatial and verbal working memory. Data were analyzed through Boosted Regression Trees and Random Forests, which are non-parametric predictive data mining techniques. The analyses yielded a user model that would predict students’ computer programming performance based on various social and cognitive variables. The results yielded that the variables, which contributed to the programming performance prediction significantly, were spatial orientation skill, spatial memory, mental orientation, self-efficacy perception and verbal memory with equal importance weights. Yet, the effect of prior knowledge and gender on programming performance has not been found to be significant. The importance of ranks of variables and the proportion of predicted variance of programming performance could be used as guidelines when designing instruction and developing curriculum.
Keywords: Improving classroom teaching, Social cognitive approach, Individual differences