skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Students’ written homework responses using digital games in Inquiry-Oriented Linear Algebra
In this report, we characterize seven of twenty-five students’ responses to a single written homework assignment from the Spring 2021 academic semester. The homework was designed to incorporate the Vector Unknown 2D digital game to investigate how students answered questions about span and linear independence after playing various levels of the game. We present our modification of the roles and characteristics framework of Zandieh et al. (2019), our identification of students’ grammatical use of game language and math language, as well as the results of analyzing students’ homework responses using our framework.  more » « less
Award ID(s):
1712524
PAR ID:
10346130
Author(s) / Creator(s):
; ;
Editor(s):
Karunakaran, S.; Higgins, A.
Date Published:
Journal Name:
Proceedings of the Annual Conference on Research in Undergraduate Mathematics Education
ISSN:
2474-9346
Page Range / eLocation ID:
54-62
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Proceedings of the Eighth ACM Conference on Learning @ Scale)
    null (Ed.)
    Structured Query Language (SQL), the standard language for relational database management systems, is an essential skill for software developers, data scientists, and professionals who need to interact with databases. SQL is highly structured and presents diverse ways for learners to acquire this skill. However, despite the significance of SQL to other related fields, little research has been done to understand how students learn SQL as they work on homework assignments. In this paper, we analyze students' SQL submissions to homework problems of the Database Systems course offered at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between every submission and their final submission to understand how students reached their final solution and how they overcame any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify interesting learning patterns and approaches. These findings will help instructors target their instruction in difficult SQL areas for the future and help students learn SQL more effectively. 
    more » « less
  2. ; ; ; (, Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)
    null (Ed.)
    As data grow both in size and in connectivity, the interest to use graph databases in the industry has been proliferating. However, there has been little research on graph database education. In response to the need to introduce college students to graph databases, this paper is the first to analyze students' errors in homework submissions of queries written in Cypher, the query language for Neo4j---the most prominent graph database. Based on 40,093 student submissions from homework assignments in an upper-level computer science database course at one university, this paper provides a quantitative analysis of students' learning when solving graph database problems. The data shows that students struggle the most to correctly use Cypher's WITH clause to define variable names before referencing in the WHERE clause and these errors persist over multiple homework problems requiring the same techniques, and we suggest a further improvement on the classification of syntactic errors. 
    more » « less
  3. ; ; ; (, s, Proceedings of the 15th International Conference on Educational Data Mining)
    Mitrovic, A; Bosch, N. (Ed.)
    Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training. 
    more » « less
  4. ; ; ; (, Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society)
    Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training. 
    more » « less
  5. ; ; ; (, Proceedings from the 15th International Conference on Educational Data Mining)
    Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-theart approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students’ responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email