Search and Justification Behavior During Multimedia Web Search for Procedural Knowledge

ABSTRACT

In an eye-tracking study, N = 38 participants performed two procedural-knowledge search tasks by using a mockup multimedia search engine results page (SERP). By presenting both conventional websites and videos as results on the SERP, we aimed at examining the role of the modality of information resources in individuals' retrieval behavior as well in their final recommendation of one most suitable information resource. Across both tasks, the results of this study indicate that participants who finally recommended a video resource spent a greater proportion of time inspecting video results on the SERP as well as on the video resources themselves. Furthermore, participants' written justifications for the recommended information resource revealed that in both tasks about one third of the participants mentioned the modality of the information resource to justify their recommendation decision. Our findings indicate that the modality of information resources at least to some extent plays a role during web search for procedural learning resources.

References

1. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., ... & Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives, abridged edition. White Plains, NY: Longman.
2. Azzopardi, L., Thomas, P., & Craswell, N. (2018). Measuring the utility of search engine result pages: an information foraging based measure. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 605--614). ACM, New York, NY, USA.
3. Bétrancourt, M., & Benetos, K. (2018). "Why and when does instructional video facilitate learning? A commentary to the special issue-developments and trends in learning with instructional video". Computers in Human Behavior, 89, 471--475.
4. Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using internet. Computers & Education, 53, 1207--1217.
5. Eickhoff, C., Teevan, J., White, R., & Dumais, S. (2014). Lessons from the journey: a query log analysis of within-session learning. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, New York, NY, USA.
6. Lewandowski, D. (2017). Users' understanding of search engine advertisements. Journal of Information Science Theory and Practice, 5, 6--25.
7. Liu, Z., Liu, Y., Zhou, K., Zhang, M., & Ma, S. (2015, August). Influence of vertical result in web search examination. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 193--202). ACM, New York, NY, USA.
8. Marchionini, G. (2019). Search, sense making and learning: closing gaps. Information and Learning Sciences, 120(1/2), 74--86.
9. Marcos, M. C., Gavin, F., & Arapakis, I. (2015). Effect of snippets on user experience in web search. In Proceedings of the XVI International Conference on Human Computer Interaction (pp. 471--478). ACM, New York, NY, USA.

Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

• Information systems

  • Information retrieval

  • World Wide Web