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Learning to read is a complex challenge during childhood, so being aware of the processes that facilitate it is essential. Decoding skills, naming speed and their relationship to reading difficulties are part of these processes. This study aims to ascertain whether there is a relationship between naming speed, cognitive variables (processing speed and working memory) and decoding skills. The sample was made up of 42 students in the first, second, third and fourth years of primary education (62% males and 38% females) between the ages of six and 10. Hierarchical regression models showed that naming times significantly predict reading pseudo-words, while no significant relationship was found with cognitive variables. The usefulness of naming tasks is shown to be a good early predictor of reading, while no direct relationship between naming times and cognitive variables is detected. [ABSTRACT FROM AUTHOR]

Highlights • A Holographic mobile-based application is used to practice the pronunciation of basic English vocabulary words. • A funny 3D holographic robot interacts as virtual teacher in charge of practicing. • An experiment with 70 Spanish pre-school children has been carried out. • Results shown a significant impact on the children's motivation and performance. Abstract This paper describes a holographic mobile-based application designed to help Spanish-speaking children to practice the pronunciation of basic English vocabulary words. The mastery of vocabulary is a fundamental step when learning a language but is often perceived as boring. Producing the correct pronunciation is frequently regarded as the most difficult and complex skill for new learners of English. In order to address these problems this research takes advantage of the power of multi-channel stimuli (sound, image and interaction) in a mobile-based hologram application in order to motivate students and improve their experience of practicing. We adapted the prize-winning HolograFX game and developed a new mobile application to help practice English pronunciation. A 3D holographic robot that acts as a virtual teacher interacts via voice with the children. To test the tool we carried out an experiment with 70 Spanish pre-school children divided into three classes, the control group using traditional methods such as images in books and on the blackboard, and two experimental groups using our drills and practice software. One experimental group used the mobile application without the holographic game and the other experimental group used the application with the holographic game. We performed pre-test and post-test performance assessments, a satisfaction survey and emotion analysis. The results are very promising. They show that the use of the holographic mobile-based application had a significant impact on the children's motivation. It also improved their performance compared to traditional methods used in the classroom. [ABSTRACT FROM AUTHOR]

Most massive open online courses (MOOC) use simple schemes for aggregating peer grades, taking the mean or the median, or compute weights from information other than the instructor's opinion about the students' knowledge. To reduce the difference between the instructor and students' aggregated scores, some proposals compute specific weights to aggregate the peer grades. In this work, we analyse the use of students' engagement and performance measures to compute personalized weights and study the validity of the aggregated scores produced by these common functions, mean, and median, together with two others from the information retrieval field, the geometric and harmonic means. To test this procedure, we have analysed data from a MOOC about Philosophy. The course had 1,059 students registered, and 91 participated in a peer review process that consisted in writing an essay and rating three of their peers using a rubric. We compared the aggregation scores obtained using weighted and nonweighted versions of the functions. Our results show that the correlation between the aggregated scores and the instructor's grades can be improved in relation to peer grading, when using the median and weights are computed according to students' performance in chapter tests. Lay Description: What is already known about this topic: Several MOOC platforms introduce peer review to grade their large number of students enrolled.Peer review makes learners evaluate and grade the work of their equals.MOOCs use unweighted aggregation schemes, for example, mean or median, or compute weights from information other than the instructor's opinion about the students' knowledge. What this paper adds: Different weighted aggregation schemes are applied and analysed to reduce the difference with the grades of a potential instructor.We compute weights proportional to students' engagement or learners' marks on questionnaires.Our approach is also compared with a recent approach for aggregating peer scores. Implications for practice and/or policy: Our approach produced marks closer to those of the instructor with regard to the unweighted aggregations and a recent approach.Contrary to other approaches, our proposal does not increase students' workload. [ABSTRACT FROM AUTHOR]

Background: 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. Method: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Educational process mining (EPM) is an emerging field in educational data mining (EDM) aiming to make unexpressed knowledge explicit and to facilitate better understanding of the educational process. EPM uses log data gathered specifically from educational environments in order to discover, analyze, and provide a visual representation of the complete educational process. This paper introduces EPM and elaborates on some of the potential of this technology in the educational domain. It also describes some other relevant, related areas such as intentional mining, sequential pattern mining and graph mining. It highlights the components of an EPM framework and it describes the different challenges when handling event logs and other generic issues. It describes the data, tools, techniques and models used in EPM. In addition, the main work in this area is described and grouped by educational application domains. WIREs Data Mining Knowl Discov 2018, 8:e1230. doi: 10.1002/widm.1230 This article is categorized under: Application Areas > Business and Industry, Application Areas > Education and Learning, Application Areas > Government and Public Sector [ABSTRACT FROM AUTHOR]

The use of Learning Management Systems has grown exponentially in the last several years and has come to have a strong effect on the teaching-learning process, particularly in higher education. The present study intends to examine students' asynchronous learning processes via an Educational Data Mining approach using data extracted from the Moodle logs of students who were grouped according to similar behaviors regarding effort, time spent working, and procrastination. The behaviors were then matched with different levels of achievement. First, the different patterns of students' involvement in the learning process in a Learning Management System were clustered. Second, the different variables selected from the Moodle records were studied to see if they were equally suitable for the configuration of student clusters. Third, the relationships between those patterns to students' final marks were examined. After analyzing the log data gathered from a Moodle 2.0 course in which 140 undergraduate students were enrolled, four different patterns of learning with different final marks were found. Additional results showed that there are variables more related to achievement and more suitable to group the students on the basis of which the different groups were characterized, namely, two Task Oriented Groups (socially or individually focused) and two Non Task Oriented Groups (procrastinators or non-procrastinators). These results have implications in the design of interventions for improving students' learning processes and achievement in LMSs. [ABSTRACT FROM AUTHOR]

In this article, some of most relevant programs of self-regulation of academic learning in the sphere of higher education were reviewed. Although there are quite a few of them, we reviewed only the interventions whose contents had been implemented in e- learning modalities or had been supported by the new information and communication technologies. For this task, we arranged the programs along a continuum that ranged from those that deal with the development of self-regulatory competences by indirect training of such competences to the programs whose impact on such competences is much more direct. Lastly, we provide information about a program that our research team is developing and implementing as a pilot study, and whose preliminary results seem highly satisfactory. [ABSTRACT FROM AUTHOR]

• Blended learning university courses provide us multiple-source and multimodal data. • Data fusion approaches improve the prediction of students' academic performance. • Ensembles and selecting the best attributes approach produce the best results. • White-box classification models provide instructors very understandable explanations. In this paper we apply data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collect and preprocess data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final exam. Our objective is to discover which data fusion approach produces the best results using our data. We carry out experiments by applying four different data fusion approaches and six classification algorithms. The results show that the best predictions are produced using ensembles and selecting the best attributes approach with discretized data. The best prediction models show us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums are the best set of attributes for predicting students' final performance in our courses. Image, graphical abstract [ABSTRACT FROM AUTHOR]

The new educational models such as smart learning environments use of digital and context‐aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye‐tracking, user logs, and click‐stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area. This article is categorized under:Application Areas > Education and Learning [ABSTRACT FROM AUTHOR]

The use of the Internet has been progressively integrated into our daily lives. This has also been true for all levels of education, where virtual learning environments have been the means by which teachers, students and educational institutions managed and distributed educational experiences. However, the present design of these systems make students have difficulties in deploying their meta-cognitive skills, in addition to producing a cognitive overload due to an inadequate content organization and navigation. Thus, it is necessary to provide learning platforms with a process that allows for the adaptation of these systems to students' characteristics, needs and context in order to enhance the teaching-learning process. This paper describes an adaptive model for Learning Management Systems (LMSs) that using variables central to the learning process allows for the application of adaptive rules to the different types of contents and knowledge to be transferred and acquired. In practice, the resulting model allows to develop adaptive courses that support and promote learning and self-regulation in virtual learning environments. [ABSTRACT FROM AUTHOR]

Homework (HW) is one of the most common school-related activities among parents, students and teachers. However, the need to assign HW to students has been extensively debated. The present investigation examines the relationship between specific HW variables (i.e. amount of HW completed, time spent on HW and perceived quality of HW time management) and academic achievement, while controlling for students’ gender and grade level. Participants included 454 students (ranging from 10 to 16 years of age) from three schools in northern Spain. A multivariate analyses of variance and path analyses showed that the amount of HW completed decreased with increased schooling, as did students’ perceived quality of HW time management. Data from hierarchical regression analyses completed by path analyses provided evidence that time spent on HW conjointly with perceived quality of HW time management explains how academic achievement is mediated by the amount of HW completed. These findings deepen the understanding of the complex impact of time spent on HW and on academic achievement. [ABSTRACT FROM PUBLISHER]

Research has suggested that the relationship between previous academic achievement and student variables is mediated by parent and teacher expectations of the child's ability and future success. The goal of this study was to analyze the mediating role of teachers' expectations and teachers' perceptions of parents' expectations between previous academic achievement and variables in students with Specific Learning Disorders (SLD) that are significant for school learning. The participants were 230 students with SLD from Spain, between 10 and 14 years old. Extrinsic variables influenced the students' intrinsic variables even more than the students' own experiences of academic success or failure. The way in which teachers in the child's academic life respond to prior results and the expectations they form can affect their instruction and, ultimately, the children's motivation, involvement, and persistence in learning. [ABSTRACT FROM AUTHOR]

This paper provides information about the efficacy of a tutorial training program intended to enhance elementary fifth graders' study processes and foster their deep approaches to learning. The program «Testas's (mis)adventures» consists of a set of books in which Testas, a typical student, reveals and reflects upon his life experiences during school years. These life stories are nothing but an opportunity to present and train a wide range of learning strategies and self-regulatory processes, designed to insure students' deeper preparation for present and future learning challenges. The program has been developed along a school year, in a one hour weekly tutorial sessions. The training program had a semi-experimental design, included an experimental group (n=50) and a control one (n=50), and used pre- and posttest measures (learning strategies' declarative knowledge, 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 of learning strategies and in their deep approach to learning, consequently lowering their use of a surface approach. In spite of this, in what concerns to academic achievement, no statistically significant differences have been found. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

The aim of this study has been to analyze the relevance of different motivational variables, behavioral and socio-educational variables about predicting the use of self regulated learning strategies. We have worked with a sample of 552 first year college students from different degrees. The results of the hierarchical regression analysis show that the use of self regulated learning strategies depends largely on the perceived self-efficacy for the use of these strategies, but also other personal variables such as interest in learning (and also to get good academic results) and perceived usefulness of using such strategies. A relevant fact was that more than the amount of time spent at work on academic tasks, it is the optimization of time spent. No significant association was found between parents' educational level and a more or less self-regulated behavior on academic work. The data have clear implications not only for the design of instructional processes in the classroom, but also for the design work required in the not-attending hours of university students. [ABSTRACT FROM AUTHOR]

The article presents a study on the underlying processes used in written compositions along with its difficulties and modalities. The study which was, take part by 326 people between 10 and 16 years of age, divided into two groups and compared by means of a writing log reveals that both the recording interval and the presence of the graphic organiser alter the processes in writing-time while there was no effect of text genre. The educational implications relevant to the study are also discussed.

The aim of the present study is to provide additional information to highlight some aspects concerning the relationship between thinking styles and academic achievement. In order to understand the extent to which thinking styles predict academic achievement, 1466 students, between 12 and 16 years old, from first to fourth grades of Compulsory Secondary Education (Spanish ESO) took part in the research. A parsimonious model of covariances was assumed in each of the four samples corresponding to the four different grades of Secondary School as well as in the total sample. Data show that thinking styles significantly explain part of the variance of academic achievement, although only about 10%. [ABSTRACT FROM AUTHOR]

To date, research on the relation between learning self-regulation and academic achievement has generally show disparate results. This work intends to look into this relation from a new perspective, which consists in classifying the students as more or less self-regulated depending on diverse indicators and using cluster analysis. The aim of this work was to identify the possible self-regulated learning profiles in a sample of university students. By means of stepwise linear regression analysis, we determined which of the selected variables better predicted metacognitive self-regulation. Then, three significantly different self-regulated learning profiles were obtained by two-step cluster analysis with those variables. Lastly, ANOVA was used to analyse the relation between the self-regulated learning profiles and academic achievement. The implications of these data for the educational practice at university are discussed. [ABSTRACT FROM AUTHOR]