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: Personalized Remedial Recommendations for SQL Programming Practice System
Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations.  more » « less
Award ID(s):
1740775
PAR ID:
10191780
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of Workshop on Adaptation and Personalization in Computer Science Education at the 28th ACM Conference on User Modeling, Adaptation and Personalization
Page Range / eLocation ID:
135 - 142
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Proceedings of Workshop on Intelligent User Interfaces for Algorithmic Transparency in Emerging Technologies at the 24th ACM Conference on Intelligent User Interfaces, IUI 2019, Los Angeles, USA, March 20, 2019)
    Recommendations for online educational systems generally differ from recommendations generated in other contexts (e.g. movies, e-commerce), given that students’ level of knowledge rather then their interests is key for suggesting the most appropriate content. Thus, the challenge of making recommendations more transparent is closely tied to how student skills are estimated and conveyed. In this paper, we present an approach based on Open Learner Model visualization as a first step for making the learning content recommendation process more transparent. A preliminary analysis of students who used the visualization for navigating the content of an introductory programming course showed that considerable time was spent exploring the explanatory interface, which could be linked to the significant likelihood of opening/attempting the recommended activities. 
    more » « less
  2. ; ; (, 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3–5, 2018)
    Developing online courses is a complex and time-consuming process that involves organizing a course into a sequence of topics and allocating the appropriate learning content within each topic. This task is especially difficult in complex domains like programming, due to the incremental nature of programming knowledge, where new topics extensively build upon domain concepts that were introduced in earlier lessons. In this paper, we propose a course-adaptive content-based recommender system that assists course authors and instructors in selecting the most relevant learning material for each course topic. The recommender system adapts to the deep prerequisite structure of the course as envisioned by a specific instructor, while unobtrusively deducing that structure from problem-solving examples that the instructor uses to present course concepts. We assessed the quality of recommendations and examined several aspects of the recommendation process by using three datasets collected from two different courses.While the presented recommender system was built for the domain of introductory programming, our course-adaptive recommendation approach could be used in a variety of other domains. 
    more » « less
  3. ; ; ; ; ; (, Lecture notes in computer science)
    This paper contributes to the research on explainable educational recommendations by investigating explainable recommendations in the context of personalized practice system for introductory Java programming. We present the design of two types of explanations to justify recommendation of next learning activity to practice. The value of these explainable recommendations was assessed in a semester-long classroom study. The paper analyses the observed impact of explainable recommendations on various aspects of student behavior and performance. 
    more » « less
  4. ; ; ; ; (, ACM Technical Symposium on Computer Science Education)
    Novice programmers can greatly improve their understanding of challenging programming concepts by studying worked examples that demonstrate the implementation of these concepts. Despite the extensive repositories of effective worked examples created by CS education experts, a key challenge remains: identifying the most relevant worked example for a given programming problem and the specific difficulties a student faces solving the problem. Previous studies have explored similar example recommendation approaches. Our research introduces a novel method by utilizing deep learning code representation models to generate code vectors, capturing both syntactic and semantic similarities among programming examples. Driven by the need to provide relevant and personalized examples to programming students, our approach emphasizes similarity assessment and clustering techniques to identify similar code problems, examples, and challenges. This method aims to deliver more accurate and contextually relevant recommendations based on individual learning needs. Providing tailored support to students in real-time facilitates better problem-solving strategies and enhances students' learning experiences, contributing to the advancement of programming education. 
    more » « less
  5. ; ; (, 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC))
    null (Ed.)
    The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email