Abstract
In this paper I report about my experience in teaching Web Information Retrieval (IR) at graduate level. I have been teaching a Web IR course for two Master’s degrees in Computer Science and Information Technology at Udine University for the last 5 years. I present the topics I am currently teaching in my course, briefly describing the syllabus, discussing the available textbooks and reading material, and term projects for students. I provide some evidence, gathered by means of a questionnaire, about students’ satisfaction with the course.
I also discuss in detail a crucial choice that every lecturer of Web IR has to make, namely whether (1) to teach the classical pre-Web IR issues first and present the Web-specific issues later, or (2) to teach directly the Web IR discipline per se. The first approach has the advantages of building on prerequisite knowledge, of presenting the historical development of the discipline, and probably appears more natural to most lecturers, who have followed the historical development of the field. Conversely, the second approach has the advantage of concentrating on a more modern view of the field, and probably leads to a higher motivation in the students, since the more appealing Web issues are dealt with at the beginning of the course. By discussing textbook support, analyzing related disciplines, and reporting on students’ and lecturers’ feedback, I provide some evidence that the first approach seems preferable.
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Notes
- 1.
I rely on the soft/hard science dichotomy. Although it has obvious limitations and it is quite rough, it is also well known (http://en.wikipedia.org/wiki/Hard_and_soft_science) and it will do here.
- 2.
I am still teaching the same course this academic year, with almost no changes and about 20 students, but I am not presenting any data from this year.
- 3.
In Italy students can try the exam when they want after course end, even in the subsequent years.
- 4.
Indeed the first and the third ones are more Web data mining than WIR books; however, the two disciplines overlap quite a lot and the boundaries between them are not so clear.
- 5.
I did not perform any deep statistical analysis of these data. I just note that using the median, instead of the mean would lead to similar considerations, and that I do not analyze variance (or interquartile range) values, nor statistical significance.
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Appendix A. The Questionnaire
Appendix A. The Questionnaire
This questionnaire aims at providing to the lecturer useful data to improve the course. The questionnaire is fully anonymous and it is not used as an evaluation of the student.
Web Information Retrieval: Final Questionnaire
The questionnaire is divided into two parts gathering, respectively, a-priori background knowledge and a-posteriori feelings and impressions on the course. Mark with a circle the numbers corresponding to your answers, remembering that:
1 means I strongly disagree
2 means I partially disagree
3 means I do not have a clear opinion
4 means I partially agree
5 means I strongly agree
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Mizzaro, S. (2011). Teaching Web Information Retrieval to Computer Science Students: Concrete Approach and Its Analysis. In: Efthimiadis, E., Fernández-Luna, J., Huete, J., MacFarlane, A. (eds) Teaching and Learning in Information Retrieval. The Information Retrieval Series, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22511-6_10
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