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Learning Analytics as a Studies Guidance Tool

At the University of Oulu, the personal tutor teacher (PSP-teachers, teacher-tutors) acts as a supporter of students’ study progress and as a guide to study paths. The personal tutor teacher is an important close contact for students in their university studies, and his/her duties include assisting the student with the development of a personal study plan, tracking student progress, and guiding the student on career advancement and career choices. Personal tutor teachers are typically lecturers, university teachers, or researchers in the same discipline and carry out their own teaching duties alongside their own work. 

As part of the Analytics AI project, the University of Oulu is developing analytical tools for personal tutor teachers, aimed at facilitating the monitoring of individual studies progress in real-time. The goal of the visualisations being developed is to give the tutor teacher a clear idea of the student's progress in relation to the student's own study plan. The tools can be used, for example, in preparing for a counselling meeting, during the counselling meeting, and more generally in studies follow-up. As we develop new tools for learning analytics, we are also researching and developing practices that leverage knowledge. In addition to the creation of tools, we need to further understand who are the tools users, and for what purposes and in which situations the tools can be used.

Next, Oulu will test the functionality of a visualization tool under the guidance of second-year students and tutor teachers. The aim is to understand how the tool can be used as a conveyor of knowledge and a basis for discussion about the progress of the studies and the students’ own study goals. One essential part of the pilot study is to create user instructions and guidance to both user groups on how to use the new tools to support studies’ guidance. As we collect feedback on the comprehensibility and meaningfulness of the views, we gain insights into the different user experiences with the tool. In order for the technology to be introduced and deployed in a sustainable fashion, it is essential to understand its operating environment.

From the student's perspective, it is important to develop tools that, as part of their guidance and study practices, support them in making choices about studying and planning. For joint student and teacher tutor meetings, it is important that the developed visualisation tools promote high-quality student-tutor interaction, rather than technical review or data mining. In that way, the tools developed can help establish real and meaningful student-tutor interaction instances. 

Authors

Anni Silvola and Riku Hietaniemi, University of Oulu

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