Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135019
Carlo Palmiero, L. Cecconi
The field of study within which this work is placed is that of data produced within digital learning environments, a field of research now known as Learning Analytics (LA). In particular, the aim is to investigate the relationship between the standard psychometric properties of the test questions and the information obtained from the log files produced during its administration, on a large scale, by computer. The results of this type of survey can help to make visible the intersections between formative assessment and summative assessment and to renew, in this way, the evaluation practices of a rapidly expanding sector such as digital education.
{"title":"Use of Learning Analytics between formative and summative assessment","authors":"Carlo Palmiero, L. Cecconi","doi":"10.20368/1971-8829/1135019","DOIUrl":"https://doi.org/10.20368/1971-8829/1135019","url":null,"abstract":"The field of study within which this work is placed is that of data produced within digital learning environments, a field of research now known as Learning Analytics (LA). In particular, the aim is to investigate the relationship between the standard psychometric properties of the test questions and the information obtained from the log files produced during its administration, on a large scale, by computer. The results of this type of survey can help to make visible the intersections between formative assessment and summative assessment and to renew, in this way, the evaluation practices of a rapidly expanding sector such as digital education.","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85883071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135027
Nan Yang, P. Ghislandi, J. Raffaghelli, G. Ritella
Engagement analytics is a branch of learning analytics (LA) that focuses on student engagement, with most studies conducted by computer scientists. Thus, rather than focusing on learning, research in this field usually treats education as a scenario for algorithms optimization and it rarely concludes with implications for practice. While LA as a research field is reaching ten years, its contribution to our understanding of teaching and learning and its impact on learning enhancement are still underdeveloped. This paper argues that data-driven modeling of engagement analytics is helpful to assess student engagement and to promote reflections on the quality of teaching and learning. In this article, the authors a) introduce four key constructs (student engagement, learning analytics, engagement analytics, modeling and data-driven modeling); b) explain why data-driven modeling is chosen for engagement analytics and the limitations of using a predefined framework; c) discuss how to use engagement analytics to promote pedagogical reflection
{"title":"Data-Driven Modeling of Engagement Analytics for Quality Blended Learning","authors":"Nan Yang, P. Ghislandi, J. Raffaghelli, G. Ritella","doi":"10.20368/1971-8829/1135027","DOIUrl":"https://doi.org/10.20368/1971-8829/1135027","url":null,"abstract":"Engagement analytics is a branch of learning analytics (LA) that focuses on student engagement, with most studies conducted by computer scientists. Thus, rather than focusing on learning, research in this field usually treats education as a scenario for algorithms optimization and it rarely concludes with implications for practice. While LA as a research field is reaching ten years, its contribution to our understanding of teaching and learning and its impact on learning enhancement are still underdeveloped. This paper argues that data-driven modeling of engagement analytics is helpful to assess student engagement and to promote reflections on the quality of teaching and learning. In this article, the authors a) introduce four key constructs (student engagement, learning analytics, engagement analytics, modeling and data-driven modeling); b) explain why data-driven modeling is chosen for engagement analytics and the limitations of using a predefined framework; c) discuss how to use engagement analytics to promote pedagogical reflection","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77843099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135047
N. Sansone, D. Cesareni
The contribution describes and problematizes the use of learning analytics within a blended university course based on a socio-constructivist approach and aimed at constructing artefacts and knowledge. Specifically, the authors focus on the assessment system adopted in the course, deliberately inspired by the principles of formative assessment: an ongoing assessment in the form of feedback shared with the students, and which integrates the teacher’s assessment with self-assessment and peer-assessment. This system obviously requires the integration of qualitative procedures from teachers and tutors and quantitative managed through the reporting functions of the LMS and online tools used for the course. The contribution ends with a reflection on the possibilities of technological development of learning analytics within the learning environment, such as to better support constructivist teaching: Learning Analytics that comes closest to social LA techniques providing the teacher with a richer picture of the student’s behaviour and learning processes.
{"title":"Which Learning Analytics for a socio-constructivist teaching and learning blended experience?","authors":"N. Sansone, D. Cesareni","doi":"10.20368/1971-8829/1135047","DOIUrl":"https://doi.org/10.20368/1971-8829/1135047","url":null,"abstract":"The contribution describes and problematizes the use of learning analytics within a blended university course based on a socio-constructivist approach and aimed at constructing artefacts and knowledge. Specifically, the authors focus on the assessment system adopted in the course, deliberately inspired by the principles of formative assessment: an ongoing assessment in the form of feedback shared with the students, and which integrates the teacher’s assessment with self-assessment and peer-assessment. This system obviously requires the integration of qualitative procedures from teachers and tutors and quantitative managed through the reporting functions of the LMS and online tools used for the course. The contribution ends with a reflection on the possibilities of technological development of learning analytics within the learning environment, such as to better support constructivist teaching: Learning Analytics that comes closest to social LA techniques providing the teacher with a richer picture of the student’s behaviour and learning processes.","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75836753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135025
A. Cadamuro, E. Bisagno, C. Pecini, L. Vezzali
Using Information and Communication Technologies (ICT) in educational environments has become widespread in latest years. Since research underlined the important role played by metacognition and self-regulation abilities in fostering learning outcomes, the relationship between these aspects appears to be particularly worthy of investigation. In this review, we present 14 studies that have deepened the relationship between ICT, metacognitive skills and learning outcomes by identifying two main categories. Some articles investigated the effects of ICT environments combined with metacognitive aspects of learning outcomes, while others investigated the reciprocal relationship between ICT and metacognition. In general, from our review, the interaction between ICT and metacognition in producing better learning outcomes appears well established and the results highlight a bi-directional relationship between metacognition and ICT, but also allow to draw attention to gaps requiring further research.
{"title":"Reflecting a… “Bit”. What relationship between metacognition and ICT?","authors":"A. Cadamuro, E. Bisagno, C. Pecini, L. Vezzali","doi":"10.20368/1971-8829/1135025","DOIUrl":"https://doi.org/10.20368/1971-8829/1135025","url":null,"abstract":"Using Information and Communication Technologies (ICT) in educational environments has become widespread in latest years. Since research underlined the important role played by metacognition and self-regulation abilities in fostering learning outcomes, the relationship between these aspects appears to be particularly worthy of investigation. In this review, we present 14 studies that have deepened the relationship between ICT, metacognitive skills and learning outcomes by identifying two main categories. Some articles investigated the effects of ICT environments combined with metacognitive aspects of learning outcomes, while others investigated the reciprocal relationship between ICT and metacognition. In general, from our review, the interaction between ICT and metacognition in producing better learning outcomes appears well established and the results highlight a bi-directional relationship between metacognition and ICT, but also allow to draw attention to gaps requiring further research.","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87809538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135030
Letizia Cinganotto, D. Cuccurullo
{"title":"Learning analytics in online social interactions. The case of a MOOC on ‘language awareness’ promoted by the European Commission","authors":"Letizia Cinganotto, D. Cuccurullo","doi":"10.20368/1971-8829/1135030","DOIUrl":"https://doi.org/10.20368/1971-8829/1135030","url":null,"abstract":"","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77018764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135041
Gerardo Fallani, Stefano Penge, Paola Tettamanti
This contribution follows the trend in educational research to collect data and create an information-based system to improve learning effectiveness. However, the value of quantitative data collected through online platforms is a subject of debate: when starting from data (inductively) meaningful interpretations are hard to discover; on the other hand, when starting from a priori schema (deductively), there is a risk of lack of flexibility and responsiveness to the changes. Hence, the need to hypothesize a different approach. For this purpose, a monitoring system whose architecture we defined as agnostic has been built and tested. That system was connected to an online learning environment with free educational resources, whose operating learning fulcrum is the Digital Learning Unit (DLU), an original theoreticalpractical device which allows interpretative assumptions to be made on the data obtainable from the system.
{"title":"An agnostic monitoring system for Italian as second language online learning","authors":"Gerardo Fallani, Stefano Penge, Paola Tettamanti","doi":"10.20368/1971-8829/1135041","DOIUrl":"https://doi.org/10.20368/1971-8829/1135041","url":null,"abstract":"This contribution follows the trend in educational research to collect data and create an information-based system to improve learning effectiveness. However, the value of quantitative data collected through online platforms is a subject of debate: when starting from data (inductively) meaningful interpretations are hard to discover; on the other hand, when starting from a priori schema (deductively), there is a risk of lack of flexibility and responsiveness to the changes. Hence, the need to hypothesize a different approach. For this purpose, a monitoring system whose architecture we defined as agnostic has been built and tested. That system was connected to an online learning environment with free educational resources, whose operating learning fulcrum is the Digital Learning Unit (DLU), an original theoreticalpractical device which allows interpretative assumptions to be made on the data obtainable from the system.","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88868070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135017
F. Agrusti, G. Bonavolontà, M. Mezzini
The dropout rates in the European countries is one of the major issues to be faced in a near future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this review is to identify studies which uses educational data mining techniques to predict university dropout in traditional courses. In Scopus and Web of Science (WoS) catalogues, we identified 241 studies related to this topic from which we selected 73, focusing on what data mining techniques are used for predicting university dropout. We identified 6 data mining classification techniques, 53 data mining algorithms and 14 data mining tools.
正如欧洲2020战略所述，欧洲国家的辍学率是不久的将来面临的主要问题之一。根据欧盟统计局的统计数据，2017年，欧盟28国平均有10.6%的年轻人(18-24岁)过早离开教育和培训。本综述的主要目的是识别使用教育数据挖掘技术来预测传统课程大学辍学率的研究。在Scopus和Web of Science (WoS)目录中，我们确定了241项与该主题相关的研究，从中选择了73项，重点关注哪些数据挖掘技术用于预测大学辍学率。我们确定了6种数据挖掘分类技术，53种数据挖掘算法和14种数据挖掘工具。
{"title":"University Dropout Prediction through Educational Data Mining Techniques: A Systematic Review","authors":"F. Agrusti, G. Bonavolontà, M. Mezzini","doi":"10.20368/1971-8829/1135017","DOIUrl":"https://doi.org/10.20368/1971-8829/1135017","url":null,"abstract":"The dropout rates in the European countries is one of the major issues to be faced in a near future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people (aged 18-24) in the EU-28 were early leavers from education and training according to Eurostat’s statistics. The main aim of this review is to identify studies which uses educational data mining techniques to predict university dropout in traditional courses. In Scopus and Web of Science (WoS) catalogues, we identified 241 studies related to this topic from which we selected 73, focusing on what data mining techniques are used for predicting university dropout. We identified 6 data mining classification techniques, 53 data mining algorithms and 14 data mining tools.","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87233805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135032
M. D. Angelis, Angelo Gaeta, F. Orciuoli, Mimmo Parente
{"title":"Improving learning with augmented reality: A didactic re-mediation model from inf@nzia digitales 3.6","authors":"M. D. Angelis, Angelo Gaeta, F. Orciuoli, Mimmo Parente","doi":"10.20368/1971-8829/1135032","DOIUrl":"https://doi.org/10.20368/1971-8829/1135032","url":null,"abstract":"","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87428557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135035
M. Polo, U. D. Iacono, G. Fiorentino, A. Pierri
In this paper we present a social analysis of the interactions among the students involved in a trial of the Italian PRIN project “Digital Interactive Storytelling in Mathematics: a Competence-based Social Approach”. The instructional design is based on collaborative scripts within a digital storytelling framework where the story follows the interactions among the characters played by the students and an expert (teacher or researcher). We report the results of a trial that involved teachers and students from the upper secondary school, analysing from a Social Network Analysis point of view the interventions of the expert, the involvement/participation of the students and the interactions among peers and with the expert. We also briefly discuss potentialities and limitations of the currently available tools to perform this kind of analysis, in view of the broader perspective offered by the Learning Analytics approach.
{"title":"A social network analysis approach to a digital interactive storytelling in mathematics","authors":"M. Polo, U. D. Iacono, G. Fiorentino, A. Pierri","doi":"10.20368/1971-8829/1135035","DOIUrl":"https://doi.org/10.20368/1971-8829/1135035","url":null,"abstract":"In this paper we present a social analysis of the interactions among the students involved in a trial of the Italian PRIN project “Digital Interactive Storytelling in Mathematics: a Competence-based Social Approach”. The instructional design is based on collaborative scripts within a digital storytelling framework where the story follows the interactions among the characters played by the students and an expert (teacher or researcher). We report the results of a trial that involved teachers and students from the upper secondary school, analysing from a Social Network Analysis point of view the interventions of the expert, the involvement/participation of the students and the interactions among peers and with the expert. We also briefly discuss potentialities and limitations of the currently available tools to perform this kind of analysis, in view of the broader perspective offered by the Learning Analytics approach.","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84210266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-12DOI: 10.20368/1971-8829/1135021
C. Bellini, A. D. Santis, Katia Sannicandro, T. Minerva
Online teaching environments acquire extremely high granularity of data, both on users’ personal profiles and on their behaviour and results. Learning Analytics (LA) is open to numerous possible research scenarios thanks to the development of technology and the speed of data collection. One characteristic element is that the data are not anonymous, but they reproduce a personalization and identification of the profiles. Identifiability of the student is implicit in the teaching process, but access to Analytics techniques reveals a fundamental question: “What is the limit?” The answer to this question should be preliminary to any use of data by students, teachers, instructors and managers of the online learning environments. In the present day, we are also experiencing a particular moment of change: the effects of the European General Data Protection Regulation (GDPR) 679/2016, the general regulation on the protection of personal data that aims to standardize all national legislation and adapt it to the new needs
{"title":"Data management in Learning Analytics: terms and perspectives","authors":"C. Bellini, A. D. Santis, Katia Sannicandro, T. Minerva","doi":"10.20368/1971-8829/1135021","DOIUrl":"https://doi.org/10.20368/1971-8829/1135021","url":null,"abstract":"Online teaching environments acquire extremely high granularity of data, both on users’ personal profiles and on their behaviour and results. Learning Analytics (LA) is open to numerous possible research scenarios thanks to the development of technology and the speed of data collection. One characteristic element is that the data are not anonymous, but they reproduce a personalization and identification of the profiles. Identifiability of the student is implicit in the teaching process, but access to Analytics techniques reveals a fundamental question: “What is the limit?” The answer to this question should be preliminary to any use of data by students, teachers, instructors and managers of the online learning environments. In the present day, we are also experiencing a particular moment of change: the effects of the European General Data Protection Regulation (GDPR) 679/2016, the general regulation on the protection of personal data that aims to standardize all national legislation and adapt it to the new needs","PeriodicalId":44748,"journal":{"name":"Journal of E-Learning and Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89289925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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