Blog

AnalytiikkaÄly Pedaforumissa

Pedaforum-päivät järjestetään Oulun yliopiston ja Oulun ammattikorkeakoulun yhteistyönä 20.-21.8.2020. Varsinaisten seminaaripäivien lisäksi keskiviikkona 19.8. järjestetään verkostotapaamisia. Tänä vuonna seminaari on maksuton ja se järjestetään täysin verkossa. Päivien teema on Pyörällä päästään – pilveen ja paikalle ja seminaarin ohjelma rakentuu jatkuvan oppimisen, avoimen oppimisen ja digitalisaation sekä kampuskokemuksen, erilaisten oppimisympäristöjen, oppijoiden ja esteettömyyden ympärille. 

Oppimisanalytiikan teema on myös vahvasti esillä päivien aikana, sillä järjestämme keskiviikkona 19.8. oppimisanalytiikan verkostotapaamisen yhteistyössä APOA-hankkeen kanssa ja hankkeellamme on seminaarissa peräti kuusi esitystä/työpajaa! Seminaarin ohjelman löydät kokonaisuudessaan täältä, mutta alta löydät kätevästi kaikki osiot, joissa AnalytiikkaÄly-hankkeemme on mukana.

Keskiviikko 19.8.2020

Oppimisanalytiikan verkostotapaaminen! Oppimisanalytiikan verkostotapaamisessa pureudutaan kahden hankkeen kuulumisiin ja tuloksiin sekä keskustellaan aktiivisesti ja osallistavasti oppimisanalytiikan ajankohtaisista tuulista.

Verkostotapaamisen ohjelma:

10.00 Tervetuloa!

10.05-12.00 Hankkeiden kuulumiset

AnalytiikkaÄly: Työkaluja oppimisanalytiikan käyttöönoton tueksi ja kurkistus tuleviin pilotteihin

APOA: Kokemuksia piloteinneista ja suositukset oppimisanalytiikan käyttöönottoon

12.00-13.00 Lounastauko

13.00-14.00 Analytiikkajaosto: oppimisanalytiikan kansalliset kuulumiset ja oppimisanalytiikan viitekehys

14.00-15.00 Työpajatyöskentelyä:  Miten tästä eteenpäin? Miten oppimisanalytiikan verkosto organisoituu? Miten oppimisanalytiikkaa voidaan edistää hankkeiden päättymisen jälkeen?

Verkostotapaaminen järjestetään Zoom. Suosittelemme tietoturvasyistä lataamaan Zoom-sovelluksen uusimman version laitteille jo etukäteen. Selaimista parhaiten Zoomia tukee Google Chrome. Verkostotapaamisessa käytämme odotusaulaa, joten liitythän tapaamiseen omalla nimelläsi. Selkokieliset Zoomin käyttöohjeet löydät täältä.

Pääset lukemaan esityksien ja työpajojen abstraktit klikkaamalla esitystä.

Torstai 20.8.2020

Rinnakkaissessio 1 klo: 14-15.30 Workshop: Opiskelija AnalytiikkaÄly dashboard hands-on

Rinnakkaissessio 1 klo:14-15.30 Workshop: Työkaluja ja toimintamalleja oppimisanalytiikkatiedon käytön tueksi: juridiikka ja johtaminen

Rinnakkaissessio 1 klo:15-15.30 Listen & Learn: Opintopolku palvelupolkuna AnalytiikkaÄly-hankkeessa

Perjantai 21.8.2020

Rinnakkaissessio 2 klo: 10.15-11.45 Workshop, Opintopolku blueprint ja oppimisanalytiikan hyödyntäminen opiskelun tukena

Rinnakkaissessio 2 klo: 10.45-11.15 Listen & Learn: Kohti oppimisanalytiikan käyttöä: omaopettajien kokemus roolistaan ja oppimisanalytiikan työpöydän käytöstä ohjauksessa

Rinnakkaissessio 3 klo:12.30-13.00 Ideas and practices: Data-driven approach to developing support processes for learning, teaching and management in higher education: Modeling factors affecting study success from large-scale study-related data

Nähdään Pedaforumissa!

Blog

What is Learning Analytics?

In Higher Education Institutions as well as in other organizations, various electronic systems are constantly leaving users with electronic traces, or data. When a student takes an electronic exam, he/she registers how long he/she took the time to answer and how many words he/she wrote. Learning environments record information such as student assignment returns and logins. The course register again accumulates course scores and grades.

Human data can be broken down into an active or a passive footprint. An active footprint is created when, for example, people write messages or leave feedback. The passive trace, on the other hand, is left to everything the user is unaware of, such as time and clicks.1

By definition, learning analytics is the process of gathering, measuring, analyzing and reporting learner-centered information with for the purpose of understanding and optimizing learning and learning environments.2 Thus, learning analytics seeks to add value to information that has been too laborious to deal with prior to analytics, to serve different user groups: students, teachers, tutors, and administration and management.

The potential for using analytics depends on what kind of applications are built around it. The digital learning platform collects data naturally and many learning environments have analytical capabilities. However, analytics can also be extended to include library card loans or even lecture attendance by adding electronic registration to lessons, for example through a mobile application. In theory, data can be collected endlessly, so it is essential to identify what information is really useful for developing learning processes.

Learning analytics can be utilized in many different ways to serve the needs of users. The analytics can be directly descriptive, whereby, for example, the student can see real-time information about the overall status of their studies or the performance of students in their teacher course. Descriptive information can be used for comparison. This allows the student to see how they have progressed relative to other students, or the teacher to see how the course implementation relates to previous rounds of the same course. Analytics also enables foresight. Data collected over a longer period of time may predict that a student who meets certain criteria is at risk of dropping out of the course, providing them with situational support. In addition, Artificial Intelligence can automatically provide students with feedback or exercises appropriate to their skill level. The list of examples is endless.

Finally, how data is presented to users in the form of various results and reports is key to the successful exploitation of learning analytics.3 The goal of visualisation is to present the information and recommendations discussed in learning analytics reporting as clearly as possible to the users.4, 5 Two examples of learning analytics’ results are presented below.

Author:

Janne Mikkola,

University of Turku

Sources

1 Madden, M.  – Fox, S. – Smith, A. – Vitak, J. (2007). Digital Footprints – Online identity management and search in the age of transparency. https://www.pewinternet.org/2007/12/16/digital-footprints/

2 Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.

3 Auvinen, A. (2017). Oppimisanalytiikka tulee – Oletko valmis? Suomen eOppimiskeskus Ry. https://poluttamo.fi/2017/08/02/oppimisanalytiikka-tulee-oletko-valmis/

4 Brown, M. (2012). Learning analytics: Moving from concept to practice. EDUCAUSE Learning Initiative, 1-5.

5 Reyes, J. A. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends, 59(2), 75-80.

Blog

Learning analytics and supporting practices

The AnalyticsAI project is developing learning analytics and supporting practices to help Higher Education Institutions to support smooth learning at different stages of studies. During the fall of 2018, we identified user needs from students, teachers, as well as faculty and university administrators. In the spring and summer of 2019, we will be moving towards application development, which will be piloted starting in autumn 2019.

Learning analytics refers to the utilisation of data, generated from learning and studying, as feedback to different user groups. While analytics has been used in many fields for a long time, it has only been used in the optimisation of education and learning in the past ten years, and with increasing emphasis in most recent years. Because learning analytics is based on the digital footprint of students, the use of analytics is closely linked to the digitalisation of education, i.e., to the use of information systems and digital environments, increasingly used in Higher Education Institutions.

Our project focuses in particular on student guidance, study design, progress monitoring and support, and leadership. Currently, most tools that utilize learning analytics have been developed to support learning and study optimization during the course. At AnalyticsAI, we focus on supporting the overall study path, in the long term.

An integral part of the use of learning analytics are the related legal issues, such as data protection, as well as the ethical aspects, such as e.g. to whom the student information should be made available within the educational institution, and what we show the student himself. Based on the needs and experiences of different user groups, we create operating models for the application of learning analytics. Particularly important aspects on the use of learning analytics, refer to student privacy, accountability and transparency of data collection and use, as well as data storage and the various methods of analysis.

Author

Anni Silvola,

University of Oulu