ML Anthology

Using POMDPs to Forecast Kindergarten Students' Reading Comprehension

Russell G. Almond, Umit Tokac, Stephanie Al Ortaiba
UAI 2012 pp. 1-7
/uai/2012/almond2012uai-using/

Abstract

Summative assessment of student abilities typically comes at the end of the instructional period, too late for educators to use the information for planning instruction. This paper explores the possibility of using Hierarchical Linear Models to forecast students end of year performance. Because these models are closely related to partially observed Markov decision processes (POMDPs), these should support extensions to instructional planning to meet educational goals. Despite the new notation, the POMDP models are subject to a familiar problem from the educational context: scale identifiability. This paper describes how this problem manifests itself and looks at one potential solution.

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Text

Almond et al. "Using POMDPs to Forecast Kindergarten Students' Reading Comprehension." Conference on Uncertainty in Artificial Intelligence, 2012.

Markdown

[Almond et al. "Using POMDPs to Forecast Kindergarten Students' Reading Comprehension." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/almond2012uai-using/)

BibTeX

@inproceedings{almond2012uai-using,
  title     = {{Using POMDPs to Forecast Kindergarten Students' Reading Comprehension}},
  author    = {Almond, Russell G. and Tokac, Umit and Al Ortaiba, Stephanie},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2012},
  pages     = {1-7},
  url       = {https://mlanthology.org/uai/2012/almond2012uai-using/}
}