An xAPI Application Profile to Monitor Self-Regulated Learning Strategies

Self-regulated learning (SRL) is being promoted and adopted increasingly due to the needs of current education, student centered and focused on competence development. One of the main components of SRL is learners’ self-monitoring, which eventually contributes to a better performance. Monitoring is also important for teachers, as it enables them to know to what extent their learners are doing well and progressing properly. At the same time, the use of technology for learning is now common and facilitates monitoring. Nevertheless, the available software still offers poor support from the SRL point of view, especially, for SRL monitoring. This clashes with the growth of learning analytics and educational data mining. The main issue is the wide variety of SRL actions that need to be captured, commonly performed in different tools, and the need to integrate them to support the development of analytics and data mining developments, making imperative the search of interoperable solutions. This paper focuses on the standardization of SRL traces to enable data collection from multiple sources and data analysis with the goal of easing the monitoring process for teachers and learners. First, the paper analyzes current monitoring software and its limitations for SRL. Then, after a brief analysis of available standards on this area, an application profile for the eXperience API specification is proposed to enable the interoperable recording of the SRL traces. The paper describes the process followed to create the profile, from the analysis to the final implementation, including the selection of the interactions that represent relevant SRL actions, the selection of vocabularies to record them and a case study.

[1]  Erik Duval,et al.  Empowering Students to Reflect on their Activity with StepUp!: Two Case Studies with Engineering Students , 2012, ARTEL@EC-TEL.

[2]  B. Zimmerman Theories of Self-Regulated Learning and Academic Achievement: An Overview and Analysis , 2001 .

[3]  Tutut Herawan,et al.  A Systematic Review on Educational Data Mining , 2017, IEEE Access.

[4]  B. Zimmerman Becoming a Self-Regulated Learner: An Overview , 2002 .

[5]  B. Zimmerman Investigating Self-Regulation and Motivation: Historical Background, Methodological Developments, and Future Prospects , 2008, American Educational Research Journal.

[6]  Manuel Mucientes,et al.  Using a learning analytics tool for evaluation in self-regulated learning , 2014, 2014 IEEE Frontiers in Education Conference (FIE) Proceedings.

[7]  Ana B. Bernardo,et al.  Implementation of training programs in self-regulated learning strategies in Moodle format: results of a experience in higher education. , 2011, Psicothema.

[8]  P. Reimann,et al.  Process mining techniques for analysing patterns and strategies in students’ self-regulated learning , 2013, Metacognition and Learning.

[9]  Antonio Bartolomé,et al.  Technologies for Self-Regulated Learning , 2011 .

[10]  Katrina Sin,et al.  Application of Big Data in Education Data Mining and Learning Analytics-A Literature Review , 2015, SOCO 2015.

[11]  Erik Duval,et al.  Learning dashboards: an overview and future research opportunities , 2013, Personal and Ubiquitous Computing.

[12]  Yongqi Gu Learning Strategies: Prototypical Core and Dimensions of Variation , 2012 .

[13]  Andy Johnson,et al.  Applying standards to systematize learning analytics in serious games , 2017, Comput. Stand. Interfaces.

[14]  Pablo Rojas-Castro Learning Analytics: A Literature Review , 2017 .

[15]  Sébastien George,et al.  TrAVis to Enhance Students’ Self-Monitoring in Online Learning Supported by Computer-Mediated Communication Tools , 2011, CISIM 2011.

[16]  Nada Dabbagh,et al.  Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning , 2012, Internet High. Educ..

[17]  José Carlos Núñez,et al.  EL APRENDIZAJE AUTORREGULADO COMO MEDIO Y META DE LA EDUCACIÓN , 2006 .

[18]  Martín Llamas Nistal,et al.  Are Learning Software Systems Well-Prepared to SupportSelf-Regulated Learning Strategies? , 2016 .

[19]  P. Pintrich,et al.  Motivational and self-regulated learning components of classroom academic performance. , 1990 .

[20]  P. Pintrich A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students , 2004 .

[21]  A. Brown Metacognition, executive control, self-regulation, and other more mysterious mechanisms , 1987 .

[22]  Marco Kalz,et al.  Time will tell: The role of mobile learning analytics in self-regulated learning , 2015, Comput. Educ..

[23]  J. González-Pienda,et al.  Evaluación de los procesos de autorregulación mediante autoinforme , 2006 .

[24]  Philip H. Winne,et al.  Studying as self-regulated learning. , 1998 .

[25]  Erik Duval,et al.  The student activity meter for awareness and self-reflection , 2012, CHI Extended Abstracts.

[26]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[27]  C. Weinstein,et al.  Learning and Study Strategies Inventory (LASSI) , 1987 .

[28]  Martín Llamas Nistal,et al.  Analysis of self-regulated learning strategies oriented to the design of software support , 2014, 2014 IEEE Frontiers in Education Conference (FIE) Proceedings.

[29]  Ido Roll,et al.  Understanding, evaluating, and supporting self-regulated learning using learning analytics , 2015, J. Learn. Anal..

[30]  Erik Duval,et al.  Tracking Actual Usage: the Attention Metadata Approach , 2007, J. Educ. Technol. Soc..