Balancing broad learning outcomes in graduate programs with detailed classroom learning outcomes is increasingly crucial in education systems. This study employs a qualitative paradigm through a case study method to address the gap between learning outcomes at the graduate program level and those at the course level. Using the ESSENTIA CURRICULUM framework—a curriculum design methodology derived from software engineering practices—we propose an innovative and adaptable approach for aligning program-wide and course-specific learning outcomes. The ESSENTIA CURRICULUM, named for its focus on the “essence of the curriculum”, is applied to the ICT for Research course within the M.Sc. program in University Teaching at the University of Nariño. This framework fosters a consistent educational journey centered on learning achievements and demonstrates its effectiveness through a comprehensive self-assessment process and stakeholder feedback. The implications of this research are twofold: it highlights the potential of adopting interdisciplinary methodologies for curriculum design and provides a scalable and alternative strategy for harmonizing learning outcomes across diverse educational contexts. By bridging principles from software engineering into education, this novel approach offers new avenues for improving curriculum coherence and applicability.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

The COVID-19 pandemic has shifted education from traditional in-person classes to remote, online-dependent learning, often resulting in reduced learning effectiveness and satisfaction due to limited face-to-face interaction. To address these challenges, interactive teaching strategies, such as the flipped classroom approach, have gained attention. The flipped classroom model emphasizes individual preparation outside class and collaborative learning during class time, relying heavily on in-person interactions. To adapt this method to remote learning, the Remote Flipped Classroom (RFC) integrates the flipped classroom approach with online learning, allowing flexibility while maintaining interactive opportunities. RFC has incorporated short films as teaching tools, leveraging their ability to contextualize knowledge and cater to the preferences of visually-driven younger learners. However, research on the effectiveness of RFC with films remains limited, particularly in fields like nursing education, where practical engagement is crucial. This article shares the practical experience of applying RFC with films in a nursing education context. Positive feedback was observed, though many students still expressed a preference for in-person classes. These insights suggest that strategies like RFC with films could be valuable in maintaining engagement and learning efficiency in remote classrooms.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

Teachers are instrumental in advancing the cognitive and motor skills of children with autism. Despite their importance, the incorporation of both educators and robotic aids in the educational frameworks of specialized schools and centers is infrequent. Extensive research has been conducted to evaluate the impact of robotic assistance on the learning outcomes for children with autism. This study investigates the effects of the Furhat robot on the educational experiences of autistic children in schools, analyzing its utility both with and without the presence of teachers. Interviews with educators were carried out to gauge the effectiveness of implementing Furhat robots in these settings. Data collected from sessions with autistic children were analyzed using ANOVA tests, offering insights into the Furhat Social Robot’s potential as a significant tool for fostering engagement and interaction. The findings highlight the robot’s effectiveness in enhancing social interaction and engagement, thereby contributing to the ongoing discussion on how social robots can improve the developmental progress and well-being of children with autism. Moreover, this paper underlines the innovative aspects of our proposed model and its wider implications. By presenting specific quantitative outcomes, our aim is to extend the reach of our findings to a broader audience. Ultimately, this research delineates significant contributions to the understanding of social robots, such as Furhat, in improving the overall well-being and developmental trajectories of children with autism.

In learning, one of the fundamental motivating factors is self-efficacy. Therefore, it is crucial to understand the level of students’ self-efficacy in learning programming. This article presents a quantitative study on undergraduate students’ perceived programming self-efficacy. 110 undergraduate computing students took part in this survey to assess programming self-efficacy. Before being given to the respondents, the survey instrument, which included a 28-item self-efficacy assessment and 30 multiple-choice programming questions, was pilot-tested. The survey instrument had a reliability of 0.755. The study results show that the students’ self-efficacy was low when they solved complex programming tasks independently. However, they felt confident when there was an assistant to guide them through the tasks. From this study, it could be concluded that self-efficacy is an essential achievement component in programming courses and can avoid education dropouts.