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Abstract Much has been written about Adaptive Learning, but does its implementation alone guarantee success? We have found that integrating an Adaptive Learning Strategy with diverse didactic techniques gives better results. The objectives of this exploratory study were to know the impact of the Adaptive Learning Strategy on students’ learning and achievement of disciplinary and transversal sub-competencies in courses supported by an Adaptive Platform in the School of Engineering and Sciences at Tecnologico de Monterrey. The assessment of the students’ and professors’ experience with an Adaptive Learning Strategy evaluated platform’s usability, teaching, learning, and engagement. The study employed a mixed methodological approach, sequential Quant- > Qual, and was quasi-experimental, with control and experimental groups. The courses that participated in the intervention were Computational Thinking, Physics I, Physics II, and Fundamental Mathematical Modeling. The findings indicated that implementing an innovation like Adaptive Learning positively impacts students’ learning and improvement when integrating elements of a flipped classroom, self-regulated learning, and micro-learning into an Adaptive Learning Strategy. The authors also propose an Implementation Model of the Adaptive Learning Strategy that has been designed by the university, implemented, and evaluated successfully.

Much has been written about Adaptive Learning, but does its implementation alone guarantee success? We have found that integrating an Adaptive Learning Strategy with diverse didactic techniques gives better results. The objectives of this exploratory study were to know the impact of the Adaptive Learning Strategy on students' learning and achievement of disciplinary and transversal sub-competencies in courses supported by an Adaptive Platform in the School of Engineering and Sciences at Tecnologico de Monterrey. The assessment of the students' and professors' experience with an Adaptive Learning Strategy evaluated platform's usability, teaching, learning, and engagement. The study employed a mixed methodological approach, sequential Quant- > Qual, and was quasi-experimental, with control and experimental groups. The courses that participated in the intervention were Computational Thinking, Physics I, Physics II, and Fundamental Mathematical Modeling. The findings indicated that implementing an innovation like Adaptive Learning positively impacts students' learning and improvement when integrating elements of a flipped classroom, self-regulated learning, and micro-learning into an Adaptive Learning Strategy. The authors also propose an Implementation Model of the Adaptive Learning Strategy that has been designed by the university, implemented, and evaluated successfully.

Despite the difficulties faced during Emergency Remote Teaching (ERT) because of the COVID-19 pandemic, it is also true that such a situation has left a series of learnings that educational institutions around the world should capitalize on. Under this scenario, interest arose in studying three delivery modalities (face-to-face, hybrid, and remote) at the university level, aiming to compare the students’ learning level and their perceptions of each delivery modality. The present study was developed in a private university in Mexico, following a quantitative methodological approach involving 360 students and 14 professors from various schools and geographical locations. Data were collected through pre-and post-tests and a perception questionnaire for students. Findings suggest that the students’ learning level in every modality varies by school and that students positively perceive the three delivery modalities, albeit identifying factors that foster and hinder their learning process in each one. The results of this study contribute to strengthening the research field on teaching during ERT, allowing educational institutions to make better decisions regarding the quality of the educational offer.

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.