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The dissertation research of M.Sc. Joni Lämsä focused on university students’ collaborative learning in a computer-supported learning environment. Lämsä found out that the computer-supported learning environment enhanced time management both for students and for teachers; the students were allowed to do a pre-organised set of tasks where and when it suited best for them.

The study provided information both about needs for support and about successful forms of scaffolding in computer-supported collaborative learning. By applying these results, teacher’s resources can be released for more individual guidance of students, which may contribute to smoother progress in university studies.

Timely support and better technologies

After the coronavirus spring, people hardly wish to engage in any stubborn controversy on technology as such. Instead, we should examine learning situations and goals to which technology can bring added value.

– In my research, I focused on students of physics, to whom technologies are not only aids for learning but also an essential part of doing physics. Problems are not solved alone using paper and pencil, but rather in groups by means of numerics, for example, Lämsä describes.

In recent years, researchers have taken interest in computer-supported collaborative learning processes. Temporal analysis of these processes is important in order to design better technologies and support learning in a timely fashion.

– Temporal analysis refers to investigating when and in what order different events occur, Lämsä explains. – For example, temporal analysis of a sport performance may confirm that the exercises pertaining to warm-up, the actual performance, and cooling down are done at the right time, he illustrates.

Resources for individual guidance

In this dissertation research, a method was developed for temporal analysis of computer-supported collaborative learning. Applying this method, Lämsä studied how different forms of scaffolding changed the learning processes.

One of these forms guided student groups to write down how they perform the different stages of problem solving. The comparison of different forms of scaffolding showed that writing helped students act in a more planned fashion, especially at the beginning of the learning process.

– In sports terms, writing reminded the groups about the meaning of warm-up before engaging in the actual performance, Lämsä compares.

– Physics and other hard sciences are struggling with interrupted studies and lengthened graduation times. Teacher resources should therefore be released for students’ individual guidance. This becomes possible when technology is used appropriately in support of learning and teaching. This will not diminish teacher’s role, but alter it, Lämsä emphasises.

This research has received funding from the Graduate School of the Faculty of Education and Psychology, Ģ��ֱ��, the MultiLeTe2 (Multidisciplinary research on learning and teaching 2) profiling project funded by the Academy of Finland, and the Finnish Cultural Foundation.

The public defence of M.Sc. Joni Lämsä’s doctoral dissertation in education, ”Developing the temporal analysis for computer-supported collaborative learning in the context of scaffolded inquiry”, takes place at the Ģ��ֱ�� on 19 September 2020, starting at 12:00 (online event: ). The opponent is Professor, PhD Margus Pedaste (University of Tartu) and the custos is Professor Raija Hämäläinen (Ģ��ֱ��). The language of the event is English.

Publication data: The dissertation is published in the JYU Dissertations series, vol. 245, 71 pages, Jyväskylä 2020, ISSN 2489-9003, ISBN 978-951-39-8248-5 (PDF). Link:

Information:

Joni Lämsä, +358408054271, joni.lamsa@jyu.fi

Publication:

JYU Dissertations number 245, 71 s., Jyväskylä 2020, ISSN 2489-9003, ISBN 978-951-39-8248-5 (PDF).

Collaborative learning of the physics students. Eetu Räsänen (left.), Henna Kokkonen, Philson Aden, Emmi Rajala ja Samuli Aalto ryhmätöissä fysiikan laitoksella. (Photo: Petteri Kivimäki)

Further information:

Joni Lämsä, +358408054271, joni.lamsa@jyu.fi 

Communications Specialist Anitta Kananen, tel. +358 40 8461395, anitta.kananen@jyu.fi

Abstract

Computer-supported collaborative learning (CSCL) frequently takes the form of inquiry-based learning (IBL) in science education. To achieve the benefits of computer-supported collaborative inquiry-based learning (CSCIL), various scaffolds have been studied from the perspective of what (not how) learning occurs and what (not how) differences emerge between the scaffolded and non-scaffolded conditions. To better address the how questions, my theoretical aim was to develop a temporal analysis procedure for CSCL. Based on a systematic literature review of 78 journal papers, I defined six key operations for the analysis of CSCL’s temporal aspects: proposing research aims regarding the temporal aspects, setting up the context, collecting process data, conceptualising events, conducting temporal analysis methods and interpreting the outcomes. A study of how the included papers performed these operations showed how the researchers implicitly conceptualised the temporal aspects of CSCL when focusing on the characteristics of or interrelations between events over time. My methodological aim was to advance temporal analysis methods to study CSCIL. My empirical aim was to design scaffolds and analyse their role in CSCIL by employing the key operations and advanced methods when groups used a numerical problem-solving tool (Python program) to inquire in undergraduate physics courses. To study how CSCIL occurs, I used video data and visualised the transitions between the IBL phases (i.e. IBL sequences) and groups’ ways of using the Python program for inquiry over time (two groups, n = 10). The identified challenges and productive practices guided the scaffold design. To study how differences emerge between the conditions (46 groups, N = 231), I performed temporal log data analysis (TLDA) and temporal lag sequential analysis (TLSA). Temporal distinctions in how the groups used the Python program between the conditions (captured by TLDA) were associated with the differences in the content and temporal emergence of IBL sequence clusters between the conditions (captured by TLSA of video data). This dissertation demonstrates how temporal analysis may advance our understanding of the premises for successful learning and benefit the design and implementation of scaffolds.