Ángel F. Agudo-Peregrina

Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying information in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are discussed next.

Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning

Abstract Widespread implementation of e-learning systems – learning management systems, virtual learning environments – across higher education institutions has aroused great interest on the study of e-learning acceptance. Acceptance studies focus on the predictors of system adoption and use, with behavioral intention to use the system as a proxy for actual use. This study proposes a TAM3-based model – with the inclusion of two additional variables: personal innovativeness in the domain of information technology and perceived interaction – to study the factors influencing the acceptance of e-learning systems. Attention is also brought towards the role of behavioral intention, especially in its relation with use behavior. In order to do so, two different settings were considered: higher education and lifelong learning; data was gathered from a survey administrated to Spanish graduate and lifelong learning students, and partial least squares analysis was used to test the research model. Results supported TAM relations, except for the intention-behavior linkage, and unveiled a dual nature of perceived usefulness – with one component related to efficiency and performance, and another component related to flexibility. The adequacy of applying TAM3-based models in educational contexts and suitability of actual system usage measures are also discussed.

Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies

Suitability of learning analytics to predict competency acquisition is assessed.Focus on two cross-curricular competencies: teamwork and commitment.Three types of student interactions are extracted from Moodle logs.No relation is found between interactions and competency levels.Recommendations for further research on this topic are offered. In the past decades, online learning has transformed the educational landscape with the emergence of new ways to learn. This fact, together with recent changes in educational policy in Europe aiming to facilitate the incorporation of graduate students to the labor market, has provoked a shift on the delivery of instruction and on the role played by teachers and students, stressing the need for development of both basic and cross-curricular competencies. In parallel, the last years have witnessed the emergence of new educational disciplines that can take advantage of the information retrieved by technology-based online education in order to improve instruction, such as learning analytics.This study explores the applicability of learning analytics for prediction of development of two cross-curricular competencies - teamwork and commitment - based on the analysis of Moodle interaction data logs in a Masters Degree program at Universidad a Distancia de Madrid (UDIMA) where the students were education professionals. The results from the study question the suitability of a general interaction-based approach and show no relation between online activity indicators and teamwork and commitment acquisition. The discussion of results includes multiple recommendations for further research on this topic.

Is age still valid for segmenting e-shoppers?

This study examines the differences in the acceptance and use of electronic commerce by end consumers, segmented in three groups according to their age. The UTAUT2 provides the theoretical framework, with the addition of three constructs from e-commerce literature: perceived risk, product risk, and perceived trust. Responses to an online survey by 817 Spanish Internet shoppers validate the research model. An omnibus test of group differences precedes the assessment of four multigroup analysis methods. Results show that gaps caused by age differences in e-shoppers are narrowing. This paper also identifies the strengths and weaknesses of the different multigroup approaches.

Revisiting the SEC Classification Framework in Traditional and Electronic Commerce

The recent growth of e-commerce sales has its cause not only on the increase of products sold, but also on the incorporation of new products which were previously not selling well on the online channel. An outstanding example of these products in Spain is clothing, which has rocketed from a marginal position in online sales to the fifth place of the top selling products over the Internet. This is an interesting case because clothing has conventionally been classified as an Experience product within the SEC (Search, Experience, Credence) classification framework, which proposes product segmentation based on the ability of consumers to identify the characteristics and attributes of the products before and after their purchase and use. This situation raises the question whether the SEC classification is still valid today and if there are changes in consumer perceptions about which segment the different products are categorized into. In order to answer these questions, a selection of 26 products was made by e-commerce experts; then, 204 undergraduate and graduate students were asked to classify those products within the SEC framework, and to declare their purchase intentions and actual shopping behavior for each product, both in the online and traditional channel. The findings from this study suggest that the SEC classification is still valid in electronic commerce but not in traditional retail stores. Moreover, the study detected actual changes in the customer perceptions of the nature of some products within the SEC classification.

Intellectual Capital Management in SMEs and the Management of Organizational Knowledge Capabilities: An Empirical Analysis

Knowledge management is a complex concept of difficult implementation in the organizations and only possible to understand from a socio-technical point of view, where the human component establishes a relation of synergy with technological and cultural elements that allow him to extend their capabilities. The proposed model is based on a cause-effect relation, as the result of the influence of IT/IS presence, organizational culture and human capital in organizational knowledge management, within the framework of a maturity model. The investigation was developed on a group of Latin American SMEs from the industrial and manufacturing sector, using a Partial Least Squares (PLS) approach and k-means clustering for statistical analysis. Structural validity of the model was verified and reliability consistency, stability, and prediction ability were demonstrated. The analysis shows that Latin American SMEs have not found yet the balance that allows them to efficiently take advantage of the installed technology and the capacity of their human resources to operate its intellectual capital.