Abstract
There is an increasing emphasis, especially in STEM areas, on students’ abilities to create explanatory descriptions. Holistic, overall evaluations of explanations can be performed relatively easily with shallow language processing by humans or computers. However, this provides little information about an essential element of explanation quality: the structure of the explanation, i.e., how it connects causes to effects. The difficulty of providing feedback on explanation structure can lead teachers to either avoid giving this type of assignment or to provide only shallow feedback on them. Using machine learning techniques, we have developed successful computational models for analyzing explanatory essays. A major cost of developing such models is the time and effort required for human annotation of the essays. As part of a large project studying students’ reading processes, we have collected a large number of explanatory essays and thoroughly annotated them. Then we used the annotated essays to train our machine learning models. In this paper, we focus on how to get the best payoff from the expensive annotation process within such an educational context and we evaluate a method called Active Learning.
The assessment project described in this article was funded, in part, by the Institute for Education Sciences, U.S. Department of Education (Grant R305G050091 and Grant R305F100007). The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.
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Notes
- 1.
The percentages are all parameters to the model. These were selected because they allowed us to see the performance of the models at a reasonable granularity. It should be noted, however, that in our case, 10% of the total set represents over 100 additional essays. In real-world settings, a smaller increment would likely be used due to the cost of annotation.
- 2.
We used a validation or holdout set to provide a consistent basis on which to judge the performance of the models.
- 3.
For what it’s worth, these are analogous to the U.S. House of Representatives and Senate, respectively, with one giving more weight to more “populous” (i.e., frequent) entities, and the other giving “equal representation” to each entity.
- 4.
Alternatively, we could have used the frequencies from the training set. We used frequencies from the remainder pool because they would be more accurate, especially at the earlier stages. In a real-life setting where the items in the remainder pool would be unlabeled, those frequencies would, of course, be unknown.
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Hastings, P., Hughes, S., Britt, M.A. (2018). Active Learning for Improving Machine Learning of Student Explanatory Essays. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_11
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