Abstract
Many learning technologies are now able to support both user-customization of the content and automated personalization of the experience based on user activities. However, there is a tradeoff between customization and personalization: the more control an educator or learner has over the parameters that define the experience, the more difficult it is to develop learning analytic models that can reliably assess learning and adapt the system accordingly. In this paper, we present a novel QE method for automatically generating a learning analytic model for the land-use planning simulation iPlan, which enables users to construct custom local simulations of socio-environmental issues. Specifically, this method employs data simulation and network analysis to construct a measurement space using nothing but log data. This space can be used to analyze users’ problem-solving processes in a context where normative measurement criteria cannot be specified in advance. In doing so, we argue that QE methods can be developed and employed even in the absence of rich qualitative data, facilitating thick(er) descriptions of complex processes based on relatively thin records of users’ activities in digital systems.
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Notes
- 1.
For readers who may be wondering why we don’t simply apply the SVD to the set of normalized vectors directly and omit the step involving the construction of a dimension for each stakeholder, this is in part because SVDs do not perform well on relatively sparse matrices, i.e., matrices in which many or most of the coefficients are zeroes [17]. Attempts to do this produced dimensions with low variance explained (generally < 3%) and poor co-registration (see §3.3). While there are many techniques specifically designed to decompose sparse matrices, we took an approach, inspired by means rotation in epistemic network analysis (ENA), that both addresses the sparse matrix problem and facilitates meaningful interpretation of the resulting space based on stakeholder preferences, which is useful given that the goal in iPlan is to maximize stakeholder satisfaction.
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Acknowledgements
This work was funded in part by the National Science Foundation (DRL-1661036, DRL-2100320, DRL-2201723, DRL-2225240), the Wisconsin Alumni Research Foundation, and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–Madison. The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.
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Ruis, A.R. et al. (2023). Thin Data, Thick Description: Modeling Socio-Environmental Problem-Solving Trajectories in Localized Land-Use Simulations. In: Arastoopour Irgens, G., Knight, S. (eds) Advances in Quantitative Ethnography. ICQE 2023. Communications in Computer and Information Science, vol 1895. Springer, Cham. https://doi.org/10.1007/978-3-031-47014-1_24
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