Learning analytics approach of EMMA project

The EMMA project provides a MOOC platform to aggregate and delivers massive open online courses (MOOC) in multiple languages from a variety of European universities. Learning analytics play an important role in MOOCs to support the individual needs of the learner.This work is funded by the EU, under the Competitiveness and Innovation Framework Program 2007-2017 (CIP) in the European Multiple MOOC Aggregator (EMMA) project. Grant Agreement no. 621030

Õpianalüütika võimalused õppimise ja õpetamise toetamisel õpetajahariduses

Info- ja kommunikatsioonitehnoloogia rakendamine ning õpihaldussüsteemide ja õpikeskkondade kasutamine õppeprotsessis on muutnud õpetajaks õppijate õpikogemusi ning õpetajahariduse õppejõudude õpetamisviise. Sellega kaasnevad erinevad digitaalsed andmed, mis annavad õppijale, õpetajale ning õppekava eestvedajale tagasisidet õppimise ja õpetamise tõhustamiseks. Haridusvaldkonnas aina enam rakendust leidev õpianalüütika võimaldab suurendada õppijate teadlikkust ja tõhusust õppeprotsessis, individualiseerida õppeprotsessi ning saada pidevat ja jooksvat tagasisidet õppimise edenemise kohta. Artikli eesmärk on analüüsida õpetajahariduse üliõpilaste ja õppejõudude ootusi õpianalüütika võimaluste suhtes, et toetada õpikeskkonnas õppimist ja õpetamist. Uurimuse teoreetilise raamistiku loob ennastjuhtiva õppija kontseptsioon. Uurimistulemused baseeruvad disainipõhisel uurimusel, kus osalusdisaini sessioonide käigus selgitati välja õpikeskkonna kasutajate (üliõpilaste, õppejõudude, õppekavade juhtide) ootused õpikeskkonna õpianalüütikarakenduste suhtes.  Summar

How can the EMMA approach to learning analytics improve employability?

In our current society there is a strong need for citizens to work on their employability and to develop key competences. Developing those competences should starts during formal education, but maintained throughout working life. MOOCs can accommodate several of the needs of the lifelong learners. EMMA facilitates learners in obtaining their personalised learning goals. And an integrated learning analytic solution will help to track the learning process and provide actionable feedback to improve, correct and ensure the achievement of students’ learning goalsThis work is funded by the EU, under the Competitiveness and Innovation Framework Program 2007-2017 (CIP) in the European Multiple MOOC Aggregator (EMMA) project. Grant Agreement no. 621030

School-University Partnership for Evidence-Driven School Improvement in Estonia

It has been acknowledged that evidence-driven practices may lead schools to improved instructional practices, student learning, or organizational improvement; still the evidence is underused by the teachers or school leaders. This study focuses on analyzing how to strengthen the evidence-driven school improvement in school-university partnership programs. Five schools learnt over a period of one school year in collaboration with the university coaches how to collect evidence in classroom and organizational level for improvement process. The results of our study illustrate profiles of the schools based on the usage of data-informed evidence, research-based evidence, or both to make decisions in the instructional and organizational level. Enablers and barriers of data use from the perspective of organizational, user, and data characteristics to implement evidence-driven practices are discussed

Promoting teachers' learning and knowledge building in a socio-technical system

The study proposes a way in which the learning and knowledge building (LKB) framework, which is consistent with the knowledge conversion phases proposed by Nonaka and Takeuchi, supports teachers’ informal and self-directed workplace learning. An LKB framework in a socio-technical system was developed to support professional development in an extended professional community. The LKB framework was implemented and formatively evaluated in the in-service course that prepares teachers for accreditation in an e-portfolio community. The extended community consisted of 16 participants, in-service teachers and domain experts. The evaluation considered (a) how the LKB practices of the framework became actualized among the community members and (b) what supported these LKB practices. Data were collected from log-files of the portfolio system. Correlation analysis and Bayesian dependency modelling revealed the way in which the bottom-up peer scaffolding from community members influences teachers’ LKB practices. As a result, the study proposes that a socio-technical system might promote LKB in a professional community

Learning analytics: European perspectives

Since the emergence of learning analytics in North America, researchers and practitioners have worked to develop an international community. The organization of events such as SoLAR Flares and LASI Locals, as well as the move of LAK in 2013 from North America to Europe, has supported this aim. There are now thriving learning analytics groups in North American, Europe and Australia, with smaller pockets of activity emerging on other continents. Nevertheless, much of the work carried out outside these forums, or published in languages other than English, is still inaccessible to most people in the community. This panel, organized by Europe’s Learning Analytics Community Exchange (LACE) project, brings together researchers from five European countries to examine the field from European perspectives. In doing so, it will identify the benefits and challenges associate

Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model-based learning analytics

Background With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction. Objective The paper reviews the current research on intelligent learning technology designed to make models of student learning and instruction transparent to teachers, an area we call model-based learning analytics. We intended to gain an insight into the coupling between the knowledge models that underpin the intelligent system and the knowledge used by teachers in their classroom decision making. Methods Using a systematic literature review methodology, we first identified 42 papers, mainly from the domain of intelligent tutoring systems and learning analytics dashboards that conformed to our selection criteria. We then qualitatively analysed the context in which the systems were applied, models they used and benefits reported for teachers and learners. Results and Conclusions A majority of papers used either domain or learner models, suggesting that instructional decisions are mostly left to teachers. Compared to previous reviews, our set of papers appeared to have a stronger focus on providing teachers with theory-driven insights and instructional decisions. This suggests that model-based learning analytics can address some of the shortcomings of the field, like meaningfulness and actionability of learning analytics tools. However, impact in the classroom still needs further research, as in half of the cases the reported benefits were not backed with evidence. Future research should focus on the dynamic interaction between teachers and technology and how learning analytics has an impact on learning and decision making by teachers and students. We offer a taxonomy of knowledge models that can serve as a starting point for designing such interaction.publishedVersio