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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

9. MPIA in Action: Example Learning Analytics

verfasst von : Fridolin Wild

Erschienen in: Learning Analytics in R with SNA, LSA, and MPIA

Verlag: Springer International Publishing

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Abstract

This chapter presents comprehensive application examples of MPIA, thereby contributing an extended, more holistic approach to learning analytics, as the included review of the state of the art will show. Already the previous chapter illustrated the general analysis workflow with the mpia software package with the help of a parsimonious-code example. This chapter now aims to extend this and showcase MPIA inaction, using increasingly more complex learning analytics applications and—in the two final ones—real-life data.

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Vorheriges Kapitel Implementation: The MPIA Package
Nächstes Kapitel Evaluation
Fußnoten
2
Note that two of the learners have been suppressed from this plot (Paul and Thomas), since their position and label is overlapping with existing learners, thus cluttering the display. Paul’s position is the same as Alba’s and Thomas’s position the same as Ida.
 
3
Note again that Paul is not depicted in Figure 9.7 above, as his position is essentially the same as Alba’s (thus cluttering the label).
 
4
Using the German stop word list provided by the tm package, which originates from the author’s lsa package.
 
5
This is to illustrate the difference to the svd in the LSA case only.
 
6
Excluding essay 41, which had ‘no activated terms’ after being mapped to the space.
 
7
The data in this study come from the British Academic Written English (BAWE) corpus, which was developed at the Universities of Warwick, Reading and Oxford Brookes under the directorship of Hilary Nesi and Sheena Gardner (formerly of the Centre for Applied Linguistics [previously called CELTE], Warwick), Paul Thompson (Department of Applied Linguistics, Reading) and Paul Wickens (Westminster Institute of Education, Oxford Brookes), with funding from the ESRC (RES-000-23-0800).
 
8
The analysis with the full data set produces more clutter in the overlay of the paths onto the map projection: Single text positions don’t have paths; and long paths sometimes produce ‘lassoing’ xsplines. The ‘lassoing’ problem could be prevented by adding a ‘travelling salesman’ optimization that changes the order of the path, so as to favour geodesics from one performance location to the next. This is, however, not trivial and is (yet) not implemented in the package. As a nice side effect of this filtering, the analysis also runs much faster.
 
9
Using the tm packages stop word list for English, which originated in the author’s lsa package.
 
Literatur
Arnold, K.: Signals: applying academic analytics. Educ. Q. 33(1), (2010)
Crespo García, R.M., Pardo, A., Delgado Kloos, C., Niemann, K., Scheffel, M., Wolpers, M.: Peeking into the black box: visualising learning activities. Int. J. Technol. Enhanc. Learn. 4(1/2), 99–120 (2012)CrossRef
Dawson, S., Bakharia, A., Heathcote, E.: SNAPP: realising the affordances of real-time SNA within networked learning environments. In: Dirckinck-Holmfeld, L., Hodgson, V., Jones, C., de Laat, M., McConnell, D., Ryberg, T. (eds.) Proceedings of the 7th International Conference on Networked Learning 2010. Aalborg, Denmark (2010)
Gardner, S., Nesi, H.: A classification of genre families in university student writing. Appl. Linguist. 34(1), 1–29 (2012)
Gerdemann, D., Burek, G., Trausan-Matu, S., Rebedea, T., Loiseau, M., Dessus, P., Lemaire, B., Wild, F., Haley, D., Anastasiou, L., Hoisl, B., Markus, T., Westerhout, E., Monachesi, P.: LTfLL Roadmap, Deliverable d2.5, LTfLL Consortium (2011)
Govaerts, S., Verbert, K., Duval, E.: Evaluating the student activity meter: two case studies. In: Proceedings of the 9th International Conference on Web-based Learning 2011. Springer, Berlin (2011)
Rebedea, T., Dascalu, M., Trausan-Matu, S., Armitt, G., Chiru, C.: Automatic assessment of collaborative chat conversations with PolyCAFe. In: Proceedings of EC-TEL 2011, pp. 299–312. (2011)
Scheffel, M., Niemann, K., Pardo, A., Leony, D., Friedrich, M., Schmidt, K., Wolpers, M., Delgado Kloos, C.: Usage pattern recognition in student activities. In: Delgado Kloos, C., Gillet, D., Crespo Garcia, R.M., Wild, F., Wolpers, M. (eds.) Towards Ubiquitous Learning. Lecture Notes in Computer Science, vol. 6964. Springer, Berlin (2012)
Trausan-Matu, S., Dessus, P., Rebedea, R., Loiseau, M., Dascalu, M., Mihaila, D., Braidman, I., Armitt, G., Smithies, A., Regan, M., Lemaire, B., Stahl, J., Villiot-Leclercq, E., Zampa, V., Chiru, C., Pasov, I., Dulceanu, A.: Support and Feedback Services Version 1.5, Deliverable d5.3, LTfLL Consortium (2010)
Metadaten
Titel
MPIA in Action: Example Learning Analytics
verfasst von
Fridolin Wild
Copyright-Jahr
2016
Buch
Learning Analytics in R with SNA, LSA, and MPIA

Print ISBN: 978-3-319-28789-8

Electronic ISBN: 978-3-319-28791-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-28791-1_9

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