Search Results (40)

Personalized learning in higher education aims to enhance student motivation, engagement, and academic outcomes. Learner personas as representations of students offer a promising approach to personalizing learning in technology-enhanced environments, particularly in combination with learning analytics (LA). This study explores how LA can be used to identify activity patterns based on data from the E-Learning Activities Recommender System (ELARS). The activity data of STEM students (N = 90) were analyzed using K-Means clustering. The analyses were based on timing, the percentage of task completion, and their combination to identify distinct engagement patterns. Based on these, six clusters (learner personas) were identified: consistent performers, overachievers, last-minute underperformers, low-engagement students, late moderate achievers, and early proactive performers. For each persona, GenAI prompts and personalized interventions based on motivational and instructional frameworks were proposed. These will inform further development of the ELARS system, with the goal of enabling personalization, promoting self-regulated learning, and encouraging students to integrate GenAI tools into their learning. The study shows how the combination of clustering techniques for learner persona development with GenAI prompt engineering and adaptive interventions has the potential to drive the design of personalized learning environments. Full article

Under the synergy of urban heritage conservation and regional cultural continuity, this study explores the spatial features of “mausoleum–city symbiosis” landscapes in Huangling County’s gully regions. Focusing on Fangzhou Ancient City, we address historical spatial degradation caused by excessive industrialization and disordered urban expansion. A methodological framework is proposed, combining low-altitude UAV-derived high-density point cloud data with RandLA-Net for semi-automatic semantic segmentation of buildings, vegetation, and roads by integrating multispectral and geometric attributes. Key findings reveal: (1) Modern buildings’ abnormal elevation in steep slopes disrupts the plateau–city visual corridor; (2) Statistical analysis shows significant morphological disparities between historical and modern streets; (3) Modern structures exceed traditional height limits, while divergent roof slopes aggravate aesthetic fragmentation. This multi-level spatial analysis offers a paradigm for quantifying historical urban spaces and validates deep learning’s feasibility in heritage spatial analytics, providing insights for balancing conservation and development in ecologically fragile areas. Full article

Educational data mining (EDM) and learning analytics (LA) are widely applied to predict student performance, particularly in determining academic success or failure. This study presents the development of a scoring algorithm for the early identification of students at risk of failing science, technology, engineering, and mathematics (STEM) courses. The proposed approach follows a structured process: First, educational data are collected, processed, and statistically analyzed. Next, numerical variables are transformed into dichotomous predictors, and their relevance is assessed using Cramér’s V measure to quantify their association with course outcomes. The final step involves constructing a scoring system that dynamically evaluates student performance over 15 weeks of instruction. Prospective validation of the model demonstrated excellent predictive performance (accuracy = 0.93, sensitivity = 0.95, specificity = 0.92), confirming its effectiveness in early risk detection. The resulting scoring algorithm is distinguished by its methodological simplicity, ease of implementation, and adaptability to different educational settings, making it a practical tool for timely interventions. Full article

With the wide adoption of Learning Management Systems (LMSs) in educational institutions, ample data have become available demonstrating students’ online behavior. Digital traces are widely applicable in Learning Analytics (LA). This study aims to explore and extract behavioral features from Moodle logs and examine their effect on undergraduate students’ performance. Additionally, traditional factors such as demographics, academic history, family background, and attendance data were examined, highlighting the prominent features that affect student performance. From January to April 2019, a total of 64,231 students’ Moodle logs were collected from a private university in Malaysia for analyzing students’ behavior. Exploratory Data Analysis, correlation, statistical tests, and post hoc analysis were conducted. This study reveals that age is found to be inversely correlated with student performance. Tutorial attendance and parents’ occupations play a crucial role in students’ performance. Additionally, it was found that online engagement during the weekend and nighttime positively correlates with academic performance, representing a 10% relative increase in the student’s exam score. Ultimately, it was found that course views, forum creation, overall assignment interaction, and time spent on the platform were among the top LMS variables that showed a statistically significant difference between successful and failed students. In the future, clustering analysis can be performed in order to reveal heterogeneous groups of students along with specific course-content-based logs. Full article

Regenerative tourism has emerged as a critical evolution from traditional approaches to sustainable tourism, which have proven insufficient to address contemporary environmental, social, and economic challenges. This study examines the case of ‘Aves de la Sierra’ in Huelva, Spain, a pioneering initiative that integrates ecosystem regeneration and community revitalisation as the core of its regenerative tourism proposal. Through the analytical theoretical framework of the Multi-Level Change Perspective (MLP) and transformative innovation, it analyses how local communities consolidate themselves as niches of innovation in regenerative tourism, autonomously managing their resources and narrative. The results of this action research process reveal a number of findings that can serve as a starting point for the dynamisation and development of other regenerative tourism experiences. These include the need for public policies aimed at strengthening these innovation niches through collaborative networks, organisational learning, and adequate funding. This article also contributes to narrowing the gap between theory and experience in regenerative tourism. Full article

Interactive multimedia experiences (IME) can be a pedagogical resource that has a strong potential to enhance learning experiences in early childhood. Learning analytics (LA) has become an important tool that allows us to understand more clearly how these multimedia experiences can contribute to the learning processes of these students. This article proposes a work route that defines a set of activities and techniques, as well as a flow for their application, by taking into consideration the importance of including LA guidelines when designing IMEs for elementary education. The work route’s graphical representation is inspired by the foundations of the Essence standard’s graphical notation language. The guidelines are grouped into five categories, namely (i) a data analytics dashboard, (ii) student data, (iii) teacher data, (iv) learning activity data, and (v) student progress data. The guidelines were validated through two approaches. The first involved a case study, where the guidelines were applied to an IME called Coco Shapes, which was aimed at transition students at the Colegio La Fontaine in Cali (Colombia), and the second involved the judgments of experts who examined the usefulness and clarity of the guidelines. The results from these approaches allowed us to obtain precise and effective feedback regarding the hypothesis under study. Our findings provide promising evidence of the value of our guidelines, which were included in the design of an IME and contributed to the greater personalized monitoring available to teachers to evaluate student learning. Full article

The field of sports analytics has grown rapidly, with a primary focus on performance forecasting, enhancing the understanding of player capabilities, and indirectly benefiting team strategies and player development. This work aims to forecast and comparatively evaluate players’ goal-scoring likelihood in four elite football leagues (Premier League, Bundesliga, La Liga, and Serie A) by mining advanced statistics from 2017 to 2023. Six types of machine learning (ML) models were developed and tested individually through experiments on the comprehensive datasets collected for these leagues. We also tested the upper 30th percentile of the best-performing players based on their performance in the last season, with varied features evaluated to enhance prediction accuracy in distinct scenarios. The results offer insights into the forecasting abilities of those leagues, identifying the best forecasting methodologies and the factors that most significantly contribute to the prediction of players’ goal-scoring. XGBoost consistently outperformed other models in most experiments, yielding the most accurate results and leading to a well-generalized model. Notably, when applied to Serie A, it achieved a mean absolute error (MAE) of 1.29. This study provides insights into ML-based performance prediction, advancing the field of player performance forecasting. Full article

Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods. Full article

Although learning analytics (LA) often processes massive data, not all courses in higher education institutions are on a large scale, such as courses for employed adult learners (EALs) or master’s students. This places LA in a new situation with small datasets. This paper explores the contemporary situation of how LA has been used for small datasets, whereby we examine how the observed LA provisions can be validated in practice, which opens up possible LA solutions for small datasets and takes a further step from previous studies to enhance this topic. By examining the field of LA, a systematic literature review on state-of-the-art LA and small datasets was conducted. Thirty relevant articles were selected for the final review. The results of the review were validated through a small-scale course for EALs at a Swedish university. The findings revealed that the methods of multiple analytical perspectives and data sources with the support of contexts and learning theories are useful for strengthening the reliability of results from small datasets. More empirical evidence is required to validate possible LA methods for small datasets. The LA cycle should be closed to be able to further assess the goodness of the models generated from small datasets. Full article

As it concerns remote laboratories (RLs) for teaching microcontroller programming, the related literature reveals several common characteristics and a common architecture. Our search of the literature was constrained to papers published in the period of 2020–2023 specifically on remote laboratories related to the subject of teaching microcontroller programming of the Arduino family. The objective of this search is to present, on the one hand, the extent to which the RL platform from the Hellenic Mediterranean University (HMU-RLP) for Arduino microcontroller programming conforms to this common architecture and, on the other hand, how it extends this architecture with new features for monitoring and assessing users’ activities over remote labs in the context of pervasive and supervised learning. The HMU-RLP hosts a great number of experiments that can be practiced by RL users in the form of different scenarios provided by teachers as activities that users can perform in their self-learning process or assigned as exercises complementary to the theoretical part of a course. More importantly, it provides three types of assessments of the code users program during their experimentation with RLs. The first type monitors each action users perform over the web page offered by the RL. The second type monitors the activities of users at the hardware level. To this end, a shadow microcontroller is used that monitors the pins of the microcontroller programmed by the users. The third type automatically assesses the code uploaded by the users, checking its similarity with the prototype code uploaded by the instructors. A trained AI model is used to this end. For the assessments provided by the HMU-RLP, the experience API (xAPI) standard is exploited to store users’ learning analytics (LAs). The LAs can be processed by the instructors for the students’ evaluation and personalized learning. The xAPI reporting and visualization tools used in our prototype RLP implementation are also presented in the paper. We also discuss the planned development of such functionalities in the future for the use of the HMU-RLP as an adaptive tool for supervised distant learning. Full article

Traditionally, educational processes were focused on learning theoretical geography content, often supplemented with hands-on activities. However, advances in technology have enabled the integration of Learning Management Systems (LMSs) such as Moodle, which enable students to learn at their own pace, receive instant and individualized feedback about their daily academic performance, and gather more daily information individually based on techniques such as Learning Analytics (LAs). Despite these benefits, there is a lack of evidence supporting this educational approach in primary education. This experimental study, involving 80 fifth-grade students, aims to address this gap while investigating the territorial and socio-economic organization of their environment and comparing two types of feedback provided: simply the correct answer (control group), and more extensive (experimental group). The findings suggest that the implementation of Moodle tasks facilitates learning, irrespective of the type of feedback provided. However, students rated activities higher in terms of usefulness and satisfaction with the teaching–learning process when extensive feedback was provided. Additionally, the daily data collected proved useful for teachers in predicting students’ final outcomes. These results highlight the potential benefits of carrying out activities in Moodle, despite their short duration, particularly at this academic level and within this knowledge domain. Full article

In the dynamic world of higher education, technological advancements are continually reshaping teaching and learning approaches, with learning analytics (LA) playing a crucial role in this transformation. This systematic literature review (SLR) explores the significant impact of LA in higher education, specifically its transformative role in personalizing and enhancing educational feedback mechanisms. Utilizing a wide range of educational data, LA facilitates a shift from generic to individualized feedback, leading to improved learning outcomes and equity. However, incorporating LA into higher education is not without challenges, ranging from data privacy concerns to the possibility of algorithmic errors. Addressing these challenges is vital for unlocking the full potential of LA. This paper also examines emerging LA trends, such as augmented reality, emotion-sensing technology, and predictive analytics, which promise to further personalize learning experiences in higher education settings. By anchoring these advancements within core educational principles, we foresee a future of education marked by innovation and diversity. This SLR provides an overview of LA’s evolution in higher education, highlighting its transformative power, acknowledging its challenges, and anticipating its future role in shaping a dynamic, responsive educational environment. Full article

Learning analytics (LA) involves collecting, processing, and visualizing big data to help teachers optimize learning conditions. Despite its contributions, LA has not yet been able to meet teachers’ needs because it does not provide sufficient actionable insights that emphasize more on analytics and less on learning. Our work uses specific analytics for student guidance to evaluate an instructional design that focuses on LA agency between teachers and students. The research goal is to investigate whether the minimal and strong guidance provided by the LA learning approach has the same impact on student outcomes. The research questions are as follows “Does the LA-based minimal and strong guidance learning approach have the same impact on student performance and SRL skills? What are the students’ learning perceptions and satisfaction under LA-based guidance?” A mixed methods study was conducted at a university in which LA-based strong guidance was applied to the experimental group and minimal guidance was given to the control group. When strong guidance was applied, the results indicated increased final grades and SRL skills (metacognitive activities, time management, persistence, and help seeking). Furthermore, student satisfaction was high with LA-based guidance. Future research could adapt our study to nonformal education to provide nuanced insights into student outcomes and teachers’ perceptions. Full article

Personalized learning is becoming more important in today’s diverse classrooms. It is a strategy that tailors instruction to each student’s abilities and interests. The benefits of personalized learning include students’ enhanced motivation and academic success. The average teacher-to-student ratio in classes is 1:15.3, making it challenging for teachers to identify each student’s areas of strength (or weakness). Learning analytics (LA), which has recently revolutionized education by making it possible to gather and analyze vast volumes of student data to enhance the learning process, has the potential to fill the need for personalized learning environments. The convergence of these two fields has, therefore, become an important area for research. The purpose of this study is to conduct a systematic review to understand the ways in which LA can support personalized learning as well as the challenges involved. A total of 40 articles were included in the final review of this study, and the findings demonstrated that LA could support personalized instruction at the individual, group, and structural levels with or without teacher intervention. It can do so by (1) gathering feedback on students’ development, skill level, learning preferences, and emotions; (2) classifying students; (3) building feedback loops with continuously personalized resources; (4) predicting performance; and (5) offering real-time insights and visualizations of classroom dynamics. As revealed in the findings, the prominent challenges of LA in supporting personalized learning were the accuracy of insights, opportunity costs, and concerns of fairness and privacy. The study could serve as the basis for future research on personalizing learning with LA. Full article

Learning analytics (LA) has the potential to significantly improve teaching and learning, but there are still many areas for improvement in LA research and practice. The literature highlights limitations in every stage of the LA life cycle, including scarce pedagogical grounding and poor design choices in the development of LA, challenges in the implementation of LA with respect to the interpretability of insights, prediction, and actionability of feedback, and lack of generalizability and strong practices in LA evaluation. In this position paper, we advocate for empowering teachers in developing LA solutions. We argue that this would enhance the theoretical basis of LA tools and make them more understandable and practical. We present some instances where process data can be utilized to comprehend learning processes and generate more interpretable LA insights. Additionally, we investigate the potential implementation of large language models (LLMs) in LA to produce comprehensible insights, provide timely and actionable feedback, enhance personalization, and support teachers’ tasks more extensively. Full article