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Teachers often rely on the use of a range of open-ended problems to assess students’ understanding of mathematical concepts. Beyond traditional conceptions of student open- ended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended work common in mathematics. While recent developments in areas of natural language processing and machine learning have led to automated methods to score student open-ended work, these methods have largely been limited to textual an- swers. Several computer-based learning systems allow stu- dents to take pictures of hand-written work and include such images within their answers to open-ended questions. With that, however, there are few-to-no existing solutions that support the auto-scoring of student hand-written or drawn answers to questions. In this work, we build upon an ex- isting method for auto-scoring textual student answers and explore the use of OpenAI/CLIP, a deep learning embedding method designed to represent both images and text, as well as Optical Character Recognition (OCR) to improve model performance. We evaluate the performance of our method on a dataset of student open-responses that contains both text- and image-based responses, and find a reduction of model error in the presence of images when controlling for other answer-level features.
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Data for analysis of consistency among undergraduate graders scoring open-ended statistics tasks
Undergraduate graders are frequently important contributors to the teaching team in post-secondary education settings. This study set out to investigate agreement for a team of undergraduate graders as they acquired training and experience for scoring responses to open-ended tasks. These data include qualitative judgments of one instructor (Rater A) and four undergraduate graders (Raters E, F, G, H) applied to student responses to four open-ended prompts about elementary statistical reasoning. The team scored several sets of student responses across 5 sessions with increasingly detailed scoring rubrics.
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- Award ID(s):
- 2236150
- PAR ID:
- 10573124
- Publisher / Repository:
- figshare
- Date Published:
- Subject(s) / Keyword(s):
- Higher education Education assessment and evaluation Forensic evaluation, inference and statistics
- Format(s):
- Medium: X Size: 1026863 Bytes
- Size(s):
- 1026863 Bytes
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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