This research full paper describes a systematic literature review that examines recent contributions to guided note-taking over the past five years. Guided notes, a widely used instructional strategy, involve providing teacher-prepared structured material to students during lectures to enhance learning outcomes. Almost 50 years of research have proven that guided notes can benefit learning, aid self-regulation and metacognition, alleviate cognitive load during note-taking, and scaffold generative learning. Previous reviews have shown the effectiveness of guided notes, with the last one focusing on aspects such as teaching modality, types of knowledge, content areas, participation, and attendance. Since this predates the COVID-19 pandemic, there is a need to understand how the field has evolved, especially in terms of modality. Inclusion and exclusion criteria for identifying relevant studies were employed. Peer-reviewed journal articles published between late 2019 and early 2024 were included, focusing on primary research addressing guided note-taking in higher education. Comprehensive searches were conducted across academic databases, such as SCOPUS, Web Of Sciences, and Taylor&Francis. The PRISMA model was followed to identify and assess eligibility and include relevant articles in the review. A table was constructed to synthesize and summarize the selected studies. The review findings are categorized according to key aspects, including the effectiveness of guided notes in higher education disciplines, their impact on student engagement, the theoretical framework behind them, and variations in implementation. Special attention is given to emerging trends and technological innovations when delivering guided notes. The review also discusses limitations identified during the synthesis process, offering insights into aspects that require further investigation. This systematic literature review provides a comprehensive overview of the recent progress in guided notes over the past five years, synthesizing existing literature to explore the effectiveness of guided notes across various disciplines, their impact on student engagement, the underlying theoretical frameworks, and the diverse implementation strategies observed. By analyzing the latest research findings and emerging trends in guided note-taking practices, this review offers valuable insights for educators, researchers, and organizations seeking to optimize student learning experiences and outcomes in higher education settings.

This research-to-practice full paper describes the implications of considering the metaverse as part of experiential learning based on computer-based instructions. The term “Meta-verse” refers to a virtual world or universe gradually becoming a reality with the advancement of immersive technologies like virtual and augmented reality. One area where the Metaverse can be applied is in education and general learning, particularly experiential learning. By providing immersive and realistic virtual environments, the Metaverse has the potential to greatly enhance experiential learning, enabling us to explore and access resources and experiences that may not be possible in the physical world. However, limited information on this emerging technology and its potential educational applications is available. This article explores the benefits and challenges of the Metaverse in education by conducting a literature review of major digital repositories. The study includes researching and analyzing existing academic literature, articles, and reflections on how the Metaverse can enhance or be used for experiential learning. As a result, the paper discusses the potential advantages and issues that need to be addressed for the Metaverse to be effectively utilized in education, such as improving access to resources and experts and increasing motivation and engagement among learners.

This research-to-practice full paper describes an experimental study investigating the benefits of note-taking to enhance pre-class content learning in a flipped classroom (FC) environment applied to an Engineering Physics course. In an FC, fundamental content learning occurs before the class (targeting low cognitive levels on Bloom's taxonomy), allowing in-class time to reinforce and apply concepts (addressing high cognitive levels on Bloom's taxonomy). However, there is a lack of empirical and controlled research studies investigating optimal strategies for obtaining high-value pre-class content learning. This study aims to contribute to this matter. Four groups are considered, each comprising an average of 40 students, following the FC instructional methodology. Pre-class activities precede the class, including reading prepared documents and watching prepared videos. In-class assessments consist of a brief multiple-choice test (maximum of 5 questions) related to the pre-class activities, aiming to evaluate low cognitive levels on Bloom's taxonomy. To enhance note-taking practices, students are encouraged to take notes, and at the beginning of the course, a video showcasing five note-taking strategies is provided. The experiment carried out along one of the chapters revised in the Engineering Physics course includes one control group and one experimental group. In the control group, students are encouraged to take notes without additional guidance, whereas in the experimental group, students receive a fill-in-the-blank style note- taking guide. The results indicate that students who engage in note-taking, irrespective of the strategy used, outperform those who do not take notes. It is well-documented that note-taking produces an improvement in the in-class learning process. Here, we show how this benefit can be translated to activities before class, enhancing self-regulation learning and reducing the cognitive load during in-class note-taking. Regarding the note-taking guide, there is no significant evidence to support the improvement of student performance. This lack of progress may be attributed to the nature of the guide, using a linear note-taking strategy that ends with non-generative notes. This study shows the benefits of note-taking in enhancing pre-class content learning in an FC environment applied to an Engineering Physics course and invites us to rethink how the note-taking guide structure could encourage the production of generative notes.

Abstract Hydropower is among the most efficient technologies to produce renewable electrical energy. Hydropower systems present multiple advantages since they provide sustainable and controllable energy. However, hydropower plants’ effectiveness is affected by multiple factors such as river/reservoir inflows, temperature, electricity price, among others. The mentioned factors make the prediction and recommendation of a station’s operational output a difficult challenge. Therefore, reliable and accurate energy production forecasts are vital and of great importance for capacity planning, scheduling, and power systems operation. This research aims to develop and apply artificial neural network (ANN) models to predict hydroelectric production in Ecuador’s short and medium term, considering historical data such as hydropower production and precipitations. For this purpose, two scenarios based on the prediction horizon have been considered, i.e., one-step and multi-step forecasted problems. Sixteen ANN structures based on multilayer perceptron (MLP), long short-term memory (LSTM), and sequence-to-sequence (seq2seq) LSTM were designed. More than 3000 models were configured, trained, and validated using a grid search algorithm based on hyperparameters. The results show that the MLP univariate and differentiated model of one-step scenario outperforms the other architectures analyzed in both scenarios. The obtained model can be an important tool for energy planning and decision-making for sustainable hydropower production.