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Content assessment has broadly improved in e-learning scenarios in recent decades. However, the e-Learning process can give rise to a spatial and temporal gap that poses interesting challenges for assessment of not only content, but also students’ acquisition of core skills such as self-regulated learning. Our objective was to discover students’ self-regulated learning processes during an e-Learning course by using Process Mining Techniques. We applied a new algorithm in the educational domain called Inductive Miner over the interaction traces from 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform’s event logs with 21,629 traces in order to discover students’ self-regulation models that contribute to improving the instructional process. The Inductive Miner algorithm discovered optimal models in terms of fitness for both Pass and Fail students in this dataset, as well as models at a certain level of granularity that can be interpreted in educational terms, which are the most important achievement in model discovery. We can conclude that although students who passed did not follow the instructors’ suggestions exactly, they did follow the logic of a successful self-regulated learning process as opposed to their failing classmates. The Process Mining models also allow us to examine which specific actions the students performed, and it was particularly interesting to see a high presence of actions related to forum-supported collaborative learning in the Pass group and an absence of those in the Fail group.

The aim of this study was to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.

The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus university teachers with and without previous knowledge). The scan-paths were registered during the viewing of video based on SRL. Subsequently, the participants were asked to solve a crossword puzzle, and relevant vs. non-relevant Areas of Interest (AOI) were defined. Conventional statistical techniques (ANCOVA) and data mining techniques (string-edit methods and k-means clustering) were applied. The former only detected differences for the crossword puzzle. However, the latter, with the Uniform Distance model, detected the participants with the most effective scan-path. The use of this technique successfully predicted 64.9% of the variance in learning results. The contribution of this study is to analyze the teaching–learning process with resources that allow a personalized response to each learner, understanding education as a right throughout life from a sustainable perspective.

This paper tests the efficacy of an intervention program in virtual format intended to train studying and self-regulation strategies in university students. The aim of this intervention is to promote a series of strategies which allow students to manage their learning processes in a more proficient and autonomous way. The program has been developed in Moodle format and hosted by the Virtual Campus of the University of Oviedo. The present study had a semi-experimental design, included an experimental group (n=167) and a control one (n=206), and used pretest and posttest measures (self-regulated learning strategies' declarative knowledge, self-regulated learning macro-strategy planning-execution-assessment, self-regulated learning strategies on text, surface and deep learning approaches, and academic achievement). Data suggest that the students enrolled in the training program, comparing with students in the control group, showed a significant improvement in their declarative knowledge, general and on text use of learning strategies, increased their deep approach to learning, decreased their use of a surface approach and, in what concerns to academic achievement, statistically significant differences have been found in favour of the experimental group.

Process mining with educational data has made use of various algorithms for model discovery, principally Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner. In this study we propose the implementation of a new algorithm for educational data called Inductive Miner. We used data from the interactions of 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform's event logs; following preprocessing, the mining was carried out on 21,629 events to discover what models the various algorithms produced and to compare their fitness, precision, simplicity and generalization. The Inductive Miner algorithm produced the best results in the tests on this dataset, especially for fitness, which is the most important criterion in terms of model discovery. In addition, when we weighted the various metrics according to their importance, Inductive Miner continued to produce the best results. Inductive Miner is a new algorithm which, in addition to producing better results than other algorithms using our dataset, also provides valid models which can be interpreted in educational terms.

The authors intended to (a) identify the association between gender or grade level and teachers' homework (HW) feedback and (b) examine the relationship between teachers' HW feedback, HW-related behaviors (e.g., amount of HW completed), and academic achievement. Four hundred fifty-four students (Grades 5-12) participated in this study. The results showed that (a) at higher grade levels, there is a lower perceived amount of teachers' HW feedback; (b) teachers' HW feedback as perceived by students is positively and significantly related to the amount of HW completed and to the perceived quality of HW time management but not to the amount of time spent on HW; (c) the amount of HW completed and the perceived quality of HW time management positively and significantly predict academic achievement; and (d) teachers' HW feedback as perceived by students has an indirect relationship with students' academic achievement by its effect on students' HW-related behaviors.

Computer-Based Learning Environments (CBLEs) have emerged as an almost limitless source of education, challenging not only students but also education providers; teaching and learning in these virtual environments requires greater self-regulation of learning. More research is needed in order to assess how self-regulation of learning strategies can contribute to better performance. This study aims to report how an Intelligent Tutoring System can help students both with and without learning difficulties to self-regulate their learning processes. A total of 119 university students with and without learning difficulties took part in an educational experiment; they spent 90 min learning in a CBLE specifically designed to assess and promote self-regulated learning strategies. Results show that as a consequence of the training, the experimental group applied more self-regulation strategies than the control group, not only as a response to a system prompt but also self-initiated. In addition, there were some differences in improvement of learning processes in students with and without learning difficulties. Our results show that when students with learning difficulties have tools that facilitate applying self-regulated learning strategies, they do so even more than students without learning difficulties.

AbstractBackground/ObjetiveThe objective of this research is threefold. First, to study the structure of the Parental Burnout Assessment (PBA); second, to learn whether parents combine the dimensions of PBA in profiles; and third, to analyze the prevalence levels of parental burnout. MethodTo address these objectives, the responses of 438 mothers and fathers were analyzed with confirmatory factor analysis and latent profile analysis. ResultsStrong evidence of validity (structural) and reliability (internal consistency) of the PBA was found. Four parental burnout profiles were identified. Moreover, from a variable-centered perspective and a person-centered perspective, very high levels of parental burnout were found. ConclusionsData indicate that the PBA is a reliable and valid instrument and suggest that practitioners may use the particular scores of the dimensions or the overall score. Likewise, the level of the four dimensions in the four parental burnout profiles (PBP) is similar within and different between profiles. Finally, the prevalence level of parental burnout is very high (over 26%) compared to data from previous studies (3.2%).

En este artículo, aplicamos técnicas de minería de datos para descubrir rutas de aprendizaje frecuentes. Hemos utilizado datos de 84 estudiantes universitarios, seguidos en un curso online usando Moodle 2.0. Proponemos agrupar a los estudiantes, en primer lugar, a partir de los datos de una síntesis de uso de Moodle y/o las calificaciones finales de los alumnos en un curso. Luego, usamos los datos de los logs de Moodle sobre cada cluster/grupo de estudiantes separadamente con el fin de poder obtener más específicos y  precisos modelos de procesos del comportamiento de los estudiantes.

Since the 1970s, a large body of research has reported on the differences between deep and surface approaches to student learning. More recently, however, this metaphor for students’ approaches to learning has been applied to the practice of teaching. Studies at the university level have identified two approaches to teaching: the information transmission/teacher-focused approach and the conceptual change/student-focused approach. The present study analyzes the relationship between teachers’ approaches to teaching and high school students’ approaches to learning. The data were analyzed by fitting a two-level structural equation model based on the hypothesis that student academic achievement is significantly determined by the way they study and that the way they study is partially determined by the way teachers teach. The participants were high school students (778 twelfth graders) enrolled in biology courses and their teachers (40 total). The same model was proposed at both levels (i.e., within and between levels) and fit the data quite well. As expected, within level, the effects of the ‘approaches to learning’ on ‘biology achievement’ regression were far larger than the corresponding effects at between level. The central findings suggest worthy directions for future research.