This Work-in-Progress paper investigates how students participating in a chemical engineering (ChE) Research Experience for Undergraduates (REU) program conceptualize and make plans for research projects. The National Science Foundation has invested substantial financial resources in REU programs, which allow undergraduate students the opportunity to work with faculty in their labs and to conduct hands-on experiments. Prior research has shown that REU programs have an impact on students’ perceptions of their research skills, often measured through the Undergraduate Research Student Self-Assessment (URSSA) survey. However, few evaluation and research studies have gone beyond perception data to include direct measures of students’ gains from program participation. This work-in-progress describes efforts to evaluate the impact of an REU on students’ conceptualization and planning of research studies using a pre-post semi-structured interview process.
The construct being investigated for this study is planning, which has been espoused as a critical step in the self-regulated learning (SRL) process (Winne & Perry, 2000; Zimmerman, 2008). Students who effectively self-regulate demonstrate higher levels of achievement and comprehension (Dignath & Büttner, 2008), and (arguably) work efficiency. Planning is also a critical step in large projects, such as research (Dvir & Lechler, 2004). Those who effectively plan their projects make consistent progress and are more likely to achieve project success (Dvir, Raz, & Shenhar, 2003).
Prior REU research has been important in demonstrating some positive impacts of REU programs, but it is time to dig deeper into the potential benefits to REU participation. Many REU students are included in weekly lab meetings, and thus potentially take part in the planning process for research projects. Thus, the research question explored here is: How do REU participants conceptualize and make plans for research projects?
The study was conducted in the ChE REU program at a large, mid-Atlantic research-oriented university during the summer of 2018. Sixteen students in the program participated in the study, which entailed them completing a planning task followed by a semi-structured interview at the start and the end of the REU program. During each session, participants read a case statement that asked them to outline a plan in writing for a research project from beginning to end. Using semi-structured interview procedures, their written outlines were then verbally described. The verbalizations were recorded and transcribed. Two members of the research team are currently analyzing the responses using an open coding process to gain familiarity with the transcripts. The data will be recoded based on the initial open coding and in line with a self-regulatory and project-management framework.
Results: Coding is underway, preliminary results will be ready by the draft submission deadline.
The methods employed in this study might prove fruitful in understanding the direct impact on students’ knowledge, rather than relying on their perceptions of gains. Future research could investigate differences in students’ research plans based on prior research experience, research intensity of students’ home institutions, and how their plans may be impacted by training.
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Comprehension First: Evaluating a Novel Pedagogy and Tutoring System for Program Tracing in CS1
What knowledge does learning programming require? Prior work has focused on theorizing program writing and problem solving skills. We examine program comprehension and propose a formal theory of program tracing knowledge based on control flow paths through an interpreter program's source code. Because novices cannot understand the interpreter's programming language notation, we transform it into causal relationships from code tokens to instructions to machine state changes. To teach this knowledge, we propose a comprehension-first pedagogy based on causal inference, by showing, explaining, and assessing each path by stepping through concrete examples within many example programs. To assess this pedagogy, we built PLTutor, a tutorial system with a fixed curriculum of example programs. We evaluate learning gains among self-selected CS1 students using a block randomized lab study comparing PLTutor with Codecademy, a writing tutorial. In our small study, we find some evidence of improved learning gains on the SCS1, with average learning gains of PLTutor 60% higher than Codecademy (gain of 3.89 vs. 2.42 out of 27 questions). These gains strongly predicted midterms (R2=.64) only for PLTutor participants, whose grades showed less variation and no failures.
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- Award ID(s):
- 1735123
- NSF-PAR ID:
- 10107748
- Date Published:
- Journal Name:
- ACM International Computing Education Research Conference
- Page Range / eLocation ID:
- 2 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3105726.3106178
