skip to main content


Title: A study of predictors for debugging quality among preservice, early childhood teachers
While there has been much progress in the meaningful integration of computer science within K-12 classrooms, there is a need to promote more equitable participation and to improve teacher preparation. One area in which this is needed is in early childhood education. In this paper, we investigated predictors of debugging quality among 19 pre-service early childhood teachers as they engaged in debugging supported by scaffolding. Bayesian regression indicated the following variables predicted debugging quality: debugging process score, English domain identification, performance approach goals, and sentiment analysis scores applied to what students wrote in response to scaffold prompts.  more » « less
Award ID(s):
1906059
NSF-PAR ID:
10178439
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Annual meeting program American Educational Research Association
ISSN:
0163-9676
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    It is critical to teach all learners to program and think through programming. But to do so requires that early childhood teacher candidates learn to teach computer science. This in turn requires novel pedagogy that can both help such teachers learn the needed skills, but also provide a model for their future teaching. In this study, we examined how early childhood teacher candidates learned to program and debug block-based code with and without scaffolding. We aimed to see how approaches to debugging vary between early childhood teacher candidates who were provided debugging scaffolds during block-based programming and those who were not. This qualitative case study focused on 13 undergraduates majoring in early childhood education. Data sources included video recording during debugging, semi-structured interviews, and (in the case of those who used scaffolding) scaffold responses. Research team members coded data independently and then came to consensus. With hypothesis-driven scaffolds, participants persisted longer. Use of scaffolds enabled the instructor to allow struggle without immediate help for participants. Collaborative reasoning was observed among the scaffolded participants whereas the participants without scaffolds often debugged alone. Regardless of scaffolds, participants often engaged in embodied debugging and also used trial and error. This study provides evidence that one can find success debugging even when engaging in trial and error. This implies that attempting to prevent trial and error may be counterproductive in some contexts. Rather, computer science educators may be advised to promote productive struggle.

     
    more » « less
  2. Debugging is an important skill all programmers must learn, including preliterate children who are learning to code in early childhood settings. Despite the fact that early learning environments increasingly incorporate coding curricula, we know little about debugging knowledge in early childhood. One reason is that the tangible programming environments designed for young children entail a layer of material complexity that we have yet to account for in terms of learning to debug. In our study of young children learning to program, we found that in the midst of solving programming tasks and learning to debug, tangible toys presented bugs of their own. This paper analyzes video of Kindergarteners learning to debug errors in the program and errors in the physical materials. We argue that concurrent physical and programming bugs present opportunities for young children to learn about the broader computational system in which they are learning to code. 
    more » « less
  3. Analogical reasoning is considered to be a critical cognitive skill in programming. However, it has been rarely studied in a block-based programming context, especially involving both virtual and physical objects. In this multi-case study, we examined how novice programming learners majoring in early childhood education used analogical reasoning while debugging block code to make a robot perform properly. Screen recordings, scaffolding entries, reflections, and block code were analyzed. The cross-case analysis suggested multimodal objects enabled the novice programming learners to identify and use structural relations. The use of a robot eased the verification process by enabling them to test their analogies immediately after the analogy application. Noticing similar functional analogies led to noticing similarities in the relation between block code as well as between block code and the robot, guiding to locate bugs. Implications and directions for future educational computing research are discussed.

     
    more » « less
  4. The specific mechanisms by which teachers and parents can provide culturally relevant opportunities for computational thinking for racially/ethnically and linguistically diverse groups of preschoolers remain unknown. Accordingly, the purpose of this research is to examine how PreK parent and teacher voice directed efforts to realize a culturally relevant computing program. We drew data sources from a subsample of design-based research meetings in which partners collaborated to co-develop the first iteration of the program. Using qualitative analysis, we examined how parent voice and teacher voice, conceptualized as perspectives and participation, influenced theories of culturally responsive computing and computational thinking in early childhood education and the translation of theory into practice in classroom and home settings. Findings showed that connecting powerful ideas from computational thinking, namely algorithms and problem solving (e.g., debugging), to familiar activities and experiences served as a powerful entry point. Yet, differences arose in how teachers and parents conceptualized culturally relevant computing and made connections to familiar routines. We discuss what can be learned from parent voice in regards to bolstering children's self-expression, access to increasingly complex computational thinking tasks, and opportunities for learning cultural and community values through computing. 
    more » « less
  5. Chua Chin Heng, Matthew (Ed.)
    Early Childhood Caries (ECC) is the most common childhood disease worldwide and a health disparity among underserved children. ECC is preventable and reversible if detected early. However, many children from low-income families encounter barriers to dental care. An at-home caries detection technology could potentially improve access to dental care regardless of patients’ economic status and address the overwhelming prevalence of ECC. Our team has developed a smartphone application (app), AICaries, that uses artificial intelligence (AI)-powered technology to detect caries using children’s teeth photos. We used mixed methods to assess the acceptance, usability, and feasibility of the AICaries app among underserved parent-child dyads. We conducted moderated usability testing (Step 1) with ten parent-child dyads using "Think-aloud" methods to assess the flow and functionality of the app and analyze the data to refine the app and procedures. Next, we conducted unmoderated field testing (Step 2) with 32 parent-child dyads to test the app within their natural environment (home) over two weeks. We administered the System Usability Scale (SUS) and conducted semi-structured individual interviews with parents and conducted thematic analyses. AICaries app received a 78.4 SUS score from the participants, indicating an excellent acceptance. Notably, the majority (78.5%) of parent-taken photos of children’s teeth were satisfactory in quality for detection of caries using the AI app. Parents suggested using community health workers to provide training to parents needing assistance in taking high quality photos of their young child’s teeth. Perceived benefits from using the AICaries app include convenient at-home caries screening, informative on caries risk and education, and engaging family members. Data from this study support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children. 
    more » « less