This work-in-progress paper details preliminary results from a qualitative study exploring faculty developers’ interactions with and perceptions of engineering instructional faculty (EIF) at Hispanic-Serving Institutions (HSIs). One potential resource for supporting EIF’s educational innovation efforts is their institutions’ center for teaching and learning (CTL). Through CTLs, and similarly named offices, faculty developers provide EIF and other faculty with professional development opportunities, such as pedagogy workshops, consultations, and seminars. By engaging in services provided by faculty developers, EIF can draw on new ideas, energy, and perspectives for instruction that they can incorporate into their beliefs and practices. This is particularly relevant at HSIs, which play a crucial role in enhancing the education of Latinx engineering students. This study aims to understand HSI faculty developers’ perceptions of EIF’s motivation to participate in professional development programming around instruction. Leveraging the self-determination theory of motivation, our preliminary results suggest that faculty developers recognize how extrinsic and intrinsic factors play an important role in EIF’s decisions to engage in instructional development programming. Based on our preliminary results, we encourage the faculty development community to leverage the identity of EIF as problem-solving engineers, identify and correct misconceptions about the role of faculty developers, and be intentional about how their programming responds to the factors intrinsically and extrinsically motivating EIF.
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This content will become publicly available on March 14, 2025
Towards Attention-Based Automatic Misconception Identification in Introductory Programming Courses
Identifying misconceptions in student programming solutions is an
important step in evaluating their comprehension of fundamental
programming concepts. While misconceptions are latent constructs
that are hard to evaluate directly from student programs, logical errors
can signal their existence in students’ understanding. Tracing
multiple occurrences of related logical bugs over different problems
can provide strong evidence of students’ misconceptions. This study
presents preliminary results of utilizing an interpretable state-ofthe-
art Abstract Syntax Tree-based embedding neural network to
identify logical mistakes in student code. In this study, we show a
proof-of-concept of the errors identified in student programs by
classifying correct versus incorrect programs. Our preliminary results
show that our framework is able to automatically identify
misconceptions without designing and applying a detailed rubric.
This approach shows promise for improving the quality of instruction
in introductory programming courses by providing educators
with a powerful tool that offers personalized feedback while enabling
accurate modeling of student misconceptions.
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- Award ID(s):
- 2236195
- NSF-PAR ID:
- 10501957
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Special Interest Group on Computer Science Education bulletin
- ISBN:
- 9798400704246
- Page Range / eLocation ID:
- 1680 to 1681
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
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