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: Can textbook annotations serve as an early predictor of student learning?
When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides no evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into their mental state—their level of comprehension, grasp of the key ideas, reading goals, etc. We explore this hypothesis via an experiment in which 198 participants read sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. However, our main goal is to examine highlighting as a data source for inferring student understanding. We explored multiple representations of the highlighting patterns and tested Bayesian linear regression and neural network models, but we found little or no relationship between a student’s highlights and quiz performance. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the mind of the reader, but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material.  more » « less
Award ID(s):
1631428
PAR ID:
10074162
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 11th International Conference on Educational Data Mining
Volume:
11
Page Range / eLocation ID:
431-437
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Proceedings of the Third International Workshop on Intelligent Textbooks (iTextbooks))
    Sosnovsky, S.; Brusilovsky, P; Baraniuk, R. G.; Lan, A. S. (Ed.)
    As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions. 
    more » « less
  2. ; ; ; (, Proceedings of the Third Workshop on Intelligent Textbooks (iTextbooks) at the 22nd International Conference on Artificial Intelligence in Education (AIED 2021))
    null (Ed.)
    As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions. 
    more » « less
  3. ; ; ; ; ; (, Intelligent Textbooks 2020)
    We investigate whether student comprehension and knowledge retention can be predicted from textbook annotations, specifically the material that students choose to highlight. Using a digital open-access textbook platform, Openstax, students enrolled in Biology, Physics, and Sociology courses read sections of their introductory text as part of required coursework, optionally highlighted the text to flag key material, and then took brief quizzes as the end of each section. We find that when students choose to highlight, the specific pattern of highlights can explain about 13% of the variance in observed quiz scores. We explore many different representations of the pattern of highlights and discover that a low-dimensional logistic principal component based vector is most effective as input to a ridge regression model. Considering the many sources of uncontrolled variability affecting student performance, we are encouraged by the strong signal that highlights provide as to a student’s knowledge state. 
    more » « less
  4. ; ; ; ; ; ; (, European Journal of Psychology and Educational Research)
    Despite the desirability of working in science, technology, engineering, and math (STEM), Black and Latinx people are underrepresented in these fields. Sustaining engagement in STEM is central to addressing the representation gap. This qualitative study examined whether and how a STEM-based after-school program (Changemakers) impacted students’ sense of engagement in STEM. Changemakers incorporates the basic tenets of STEM engagement and a purpose curriculum to increase students' sense of engagement. Purpose is an aspiration towards future-oriented goals, active engagement with one’s goals, and intention to contribute to the world. The sample was composed of students, ages 15-17 years old (N=10, 5=M; 5=F), from a public, low-income high school. Findings suggested that three elements helped engage participants with STEM material: challenging and novel curriculum, experiential learning, and supportive relationships. These findings underscore additional STEM programs can enhance their student’s learning and connection to the field by ensuring that their program encapsulates these identified components. 
    more » « less
  5. ; (, International Conference on Artificial Intelligence in Education)
    Abstract. Game-based learning environments (GBLEs) are often criticized for not offering adequate support for students when learning and problem solving within these environments. A key aspect of GBLEs is the verbal representation of information such as text. This study examined learners’ metacognitive judgments of informational text (e.g., books and articles) through eye gaze behaviors within CRYSTAL ISLAND (CI). Ninety-one undergraduate students interacted with game elements during problem-solving in CI, a GBLE focused on facilitating the development of self-regulated learning (SRL) skills and domain-specific knowledge in microbiology. The results suggest engaging with informational text along with other goal-directed actions (actions needed to achieve the end goal) are large components of time spent within CI. Our findings revealed goal-directed actions, specifically reading informational texts, were significant predictors of participants’ proportional learning gains (PLGs) after problem solving with CI. Additionally, we found significant differences in PLGs where participants who spent a greater time fixating and reengaging with goal- relevant text within the environment demonstrated greater proportional learning after problem solving in CI. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email