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: Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning
Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners’ performance in instructional technologies given only the technology itself without fitting any parameters to existing learners’ data. While these so call “zero-parameter” models have been successful in modeling student learning in intelligent tutoring systems they still show systematic deviation from human learning performance. One deviation stems from the computational models’ lack of prior knowledge—all models start off as a blank slate—leading to substantial differences in performance at the first practice opportunity. In this paper, we explore three different strategies for accounting for prior knowledge within computational models of learning and the effect of these strategies on the predictive accuracy of these models.  more » « less
Award ID(s):
1824257
PAR ID:
10174635
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019)
Page Range / eLocation ID:
456 - 461
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, Proceedings of the 43rd Annual Conference of the Cognitive Science Society)
    Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K. (Ed.)
    Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance. 
    more » « less
  2. ; ; ; ; ; ; ; (, Annual Meeting of the International Society of the Learning Sciences–ISLS 2021)
    de Vries, E.; Ahn, J.; Hod, Y. (Ed.)
    Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 
    more » « less
  3. ; ; ; ; ; ; ; (, Annual Meeting of the International Society of the Learning Sciences–ISLS 2021.)
    de Vries, E.; Ahn, J.; Y. Hod, Y. (Ed.)
    Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 
    more » « less
  4. ; ; ; ; (, Proceedings of the 52nd ACM Technical Symposium on Computer Science Education)
    null (Ed.)
    We present in this paper the results of a randomized control trial experiment that compared the effectiveness of two instructional strategies that scaffold learners' code comprehension processes: eliciting Free Self-Explanation and a Socratic Method. Code comprehension, i.e., understanding source code, is a critical skill for both learners and professionals. Improving learners' code comprehension skills should result in improved learning which in turn should help with retention in intro-to-programming courses which are notorious for suffering from very high attrition rates due to the complexity of programming topics. To this end, the reported experiment is meant to explore the effectiveness of various strategies to elicit self-explanation as a way to improve comprehension and learning during complex code comprehension and learning activities in intro-to-programming courses. The experiment showed pre-/post-test learning gains of 30% (M = 0.30, SD = 0.47) for the Free Self-Explanation condition and learning gains of 59% (M = 0.59,SD = 0.39) for the Socratic method. Furthermore, we investigated the behavior of the two strategies as a function of students' prior knowledge which was measured using learners' pretest score. For the Free Self-Explanation condition, there was no significant difference in mean learning gains for low vs. high knowledge students. The magnitude of the difference in performance (mean difference= 0.02,95% CI: -0.34 to 0.39) was very small (eta squared = 0.006). Likewise, the Socratic method showed no significant difference in mean learning gains between low vs. high performing students. The magnitude of the performance difference (mean difference =-0.24,95% CI: -0.534 to 0.03) was large (eta squared = 0.10). These findings suggest that eliciting self-explanations can be used as an effective strategy and that guided self-explanations as in the Socratic method condition is more effective at inducing learning gains. 
    more » « less
  5. ; ; (, Journal of Hospitality & Tourism Education)
    This paper reviews the state-of-art articles on instructional technologies for hospitality and tourism education. The types of technologies, roles in instruction, theoretical underpinnings, assessments, benefits, and challenges are synthesized. Education context, knowledge, skills, and attitudes developed through instructional technologies are also explored. Virtual reality, virtual games, social media, online courses, and simulations have been primarily used. Instructional technologies were applied to facilitate content delivery, practice, communication, assessments, feedback, and authentic learning experiences. Most studies did not ground their educational technologies in defensible learning theories. Only a limited number of studies employed adoption theories to examine the intention of using technologies. Further studies should investigate whether certain instructional technologies provide significant benefits to learning relative to their costs. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email