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The growth of NZ’s software industry is essential for NZ’s vision of a productive economy. With 12,000 firms, 35,000 employees, a $5.3Bn contribution to GDP and a strong export focus, the software sector is a high-value sector.

Underpinning this economic value are highly skilled professionals. To build high-quality software, 'transferable' skills, including team and intercultural skills, communication, negotiation and empathy are significant. However, many ICT graduates and professionals lack these skills, and teaching them is expensive and time-consuming.

This research aims to develop an online, interactive and personalised learning approach for transferable skills based on 'active' video watching. Novel scientific contributions are:

1. AI-based model to provide personalized support and learning during video-watching
2. An interaction model for engagement with video-based learning material that integrates interactive activities to ensure consistent, active engagement based on a learner’s profile
3. A computer-based training platform geared towards transferable skills relevant to different types of ICT roles based on scientific rigor and practical relevance.

We will identify psychological and cognitive factors that facilitate video-based learning of transferable skills and define skills suitable for video-based learning. The research will enhance conventional passive video watching with novel mechanisms to increase learning experience, and push beyond a conventional classroom setting into professional development in industry.

The research will help small and medium-sized ICT companies in NZ train employees in an effective and time-efficient manner, resulting in quantified gains in productivity and economic performance. In particular, it will extend reach and accessibility to more effective training and lower barriers to adoption.

Our research will reinforce NZ’s position as an international leader in AI in education, and software engineering research, science and technology.

AVW-Space, an Active Video Watching platform

In collaboration with Professor Vania Dimitrova from the University of Leeds (UK), we have developed the AVW-Space platform, a controlled video-watching platform that supports active learning.

AVW-Space supports active video watching by providing note-taking, interactive visualisations and personalised nudges.

Our goal was to develop a platform for improving students’ transferable skills, although AVW-Space can be used for teaching other types of skills, including supporting flipped classrooms and other forms of video-based learning.
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To create an instance of AVW-Space, the teacher needs to select publicly available videos from YouTube.

Interaction with AVW-Space happens in two phases:

In Phase 1, students watch and comment on videos individually, in Private Space. In order to comment on a video, the student needs to use one of the aspects, defined by the teacher to focus students on reflecting on their previous experience.

In Phase 2, students can review and rate each others’ comments (anonymized), using rating categories defined by the teacher:

AVW-Space has been used in 15+ studies at the ΢��ҕ�l, as well as in studies by researchers from Cyprus and Sweden. We have performed several studies with an insanitation of AVW-Space for teaching presentation skills.

The new development in AVW-Space is intelligent nudges that support students in writing high-quality comments:

Presentation: Developing Personalised Nudges for Improving Comments Quality

For More Information

Please see the publication list below.

Please contact Professor Tanja Mitrovic (Tanja.mitrovic@canterbury.ac.nz) if you would like to try AVW-Space.

Team

Invited Talk

Tanja gave an invited talk at ICCE 2018, titled 'Towards personalised support for learning transferable skills via active video watching'.

Acknowledgments

Research and development of AVW-Space was supported by two regional grants from the��, an ImREAL grant (EU-FP7-ICT-257184), as well as funding from the ΢��ҕ�l.

Publications

1. Mohammadhassan, N., Mitrovic, A., (2021) Providing personalised nudges for improving comments quality in active video watching. 11th��International Conference on Learning Analytics & Knowledge LAK 2021, Companion proceedings, demo.
2. Mohammed, A., Dimitrova, V. (2020) Characterising video segments to support learning. In: So, H.J. et al. (Eds.) Proceedings of the 28th International Conference on Computers in Education, pp. 11-20. Asia-Pacific Society for Computers in Education.
3. Mohammadhassan, N., Mitrovic, A., Neshatian, K., Dunn, J. (2020) Automatic quality assessment of comments in active video watching using machine learning techniques. In: So, H.J. et al. (Eds.) Proceedings of the 28th��International Conference on Computers in Education, pp. 1-10. Asia-Pacific Society for Computers in Education. ISBN978-986-97214-5-5. Nominated for the Best overall paper and Best student paper awards.
4. Mitrovic, A., Gordon, M., Piotrkowicz, A., Dimitrova, V. (2019) Investigating the effect of adding nudges to increase engagement in active video watching. In: S. Isotani et al. (Eds.) Proc. 20th��Conf. AIED 2019, LNAI 11625, pp. 320-332, Springer Nature Switzerland.
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5. Taskin, Y., Hecking, T., Hoppe, H.U., Dimitrova, V., Mitrovic, A. (2019) Characterizing comment types and levels of engagement in video-based learning as a basis for adaptive nudging. ECTEL 2019, Delft, 16-19 September 2019, LCNS 11722, pp. 362-376.
6. Mitrovic, A., Gordon, M., Piotrkowicz, A., Dimitrova, V. (2019) Investigating the effect of adding nudges to increase engagement in active video watching. Proc. 20th Int. Conf. Artificial Intelligence in Education AIED 2019, LNAI 11625, pp. 320-332, Springer Nature Switzerland. https://link.springer.com/chapter/10.1007%2F978-3-030-23204-7_27
7. Abolkasim, E., Lau, L., Mitrovic, A., Dimitrova, V. (2018) Ontology-based domain diversity profiling of user comments. In: C. Penstein Rose et al. (Eds.) Proceedings of the 19th��Conf. Artificial Intelligence in Education, Part II, Springer, LNAI 10948, pp 3-8, London, 27-30.6.2018.
8. Piotrkowicz, A., Dimitrova, V., Mitrovic, A., Lau, L. (2018) Self-Regulation, Knowledge, Experience: Which Characteristics are Useful to Predict User Engagement? HAAPIE workshop, Adjunct Proceedings of the 26th��ACM UMAP conference, Singapore 8-11 July 2018, pp. 63-68.
9. Piotrkowicz, A., Dimitrova, V., Mitrovic, A., Lau, L. (2018) Using the Explicit User Profile to Predict User Engagement in Active Video Watching. Proc. 26th��Conf. UMAP 2018, pp. 365-366, Singapore, 8-11.7.2018.
10. Sjödén B., Dimitrova V., Mitrovic A. (2018) Using Thematic Analysis to Understand Students’ Learning of Soft Skills from Videos. In: Pammer-Schindler V., Pérez-Sanagustín M., Drachsler H., Elferink R., Scheffel M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science, vol 11082. Springer, Cham (pp 656-659)��
    
11. Abolkasim E., Lau L., Dimitrova V., Mitrovic A. (2018) Diversity Profiling of Learners to Understand Their Domain Coverage While Watching Videos. In: Pammer-Schindler V., Pérez-Sanagustín M., Drachsler H., Elferink R., Scheffel M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science, vol 11082. Springer, Cham (pp 561-565).
    
12. Hecking, T., Dimitrova, V., Mitrovic, A., Hoppe, U. (2017) Using Network-Text analysis to characterise learner engagement in active video watching. In: Chen W. et al. (Eds), Proceedings of the 25th��International Conference on Computers in Education ICCE 2017, Christchurch, 4-9 December 2017, pp. 326-335. Asia-Pacific Society for Computers in Education.��Best Technical Design Paper Award
    
13. Mitrovic, A., Gostomski, P., Herritsch, A., Dimitrova, V. (2017) Improving presentation skills of first-year engineering students using Active Video Watching. In: N. Huda, D. Inglis, N. Tse, G. Town (Eds.) Proceedings of the 28th��Annual Conference of the Australasian Association for Engineering Education (AAEE 2017), Sydney, 10-13 December 2017, pp. 809-816.
14. Galster, M., Mitrovic, A., and Gordon. M. (2018). Toward Enhancing the Training of Software Engineering Students and Professionals Using Active Video Watching. In Proceedings of 40th��International Conference on Software Engineering: Software Engineering Education and Training Track, Gothenburg, Sweden, May27-June 3 2018 (ICSE-SEET’18), pp. 5-8, ACM.
15. Dimitrova, V., Mitrovic, A., Piotrkowicz, A., Lau, L., Weerasinghe, A. (2017) Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching. In: Bielikova, M., Herder, E., Cena, F., Desmarais, M. (Eds.) Proc. 25th��ACM UMAP conference, Bratislava, Slovakia, 9-12 July 2017, pp. 22-31.
    , ACM.
    
16. Mitrovic, A., Dimitrova, V., Lau, L., Weerasinghe, A., Mathews, M. (2017) Supporting Constructive Video-based Learning: Requirements Elicitation from Exploratory Studies. In: E. Andre, R. Baker, X. Hu, M. Rodrigo, B. du Boulay (Eds.), Proc. 18th��Conf. Artificial Intelligence in Education, LNAI 10331, pp. 224-237
17. Mitrovic, A., Dimitrova, V., Weerasinghe, A., Lau, L. (2016) Reflexive experiential learning: using active video watching for soft skills training. In: Chen, W. et al. (Eds.) Proc. 24th��Conf. Computers in Education, pp. 192-201. Mumbai, India, Nov 28 – Dec 2 2016. Asia-Pacific Society for Computers in Education (ASPCE).
18. Lau, L., Mitrovic, A., Weerasinghe, A. and Dimitrova, V. (2016) Usability of an Active Video Watching System for Soft Skills Training. Zagreb, Croatia: Proc. 1st Int. workshop on Intelligent Mentoring Systems IMS 2016 held in conjunction with ITS 2016, 7-10 June 2016.