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Transfer of learning theory explains how learners can apply their previously acquired knowledge and skills in a new situation or context. In the context of writing transfer and lab report writing, first-year writing courses can act as one kind of previous learning experience or as a transfer source, and lower-division engineering labs can be the new situation or the transfer target. This preliminary study investigates how engineering students’ prior writing experience affects their lab report writing in lower-division introductory engineering labs. This study uses two distinct sites of first-year writing-intensive courses: one rhetorically-focused and one literature/philosophy-focused. We collected student samples (n = 9) from three universities offering these two distinct sites and approaches. We compared the content, outcomes, and writing expectations of the first-year writing-intensive courses offered by the three schools. Next, we conducted a rhetorical analysis of research papers collected from the writing-intensive course samples to identify each site's writing knowledge and skills. The same analysis was applied to the student’s first lab reports collected from the introductory engineering lab courses. We then compared the writing knowledge and skills between the first-year writing-intensive course samples and the engineering lab report samples to investigate how learning transfer occurred in the student writing at these three different sites. The criteria used to conduct the rhetorical analysis of writing samples focuses on writing outcomes most relevant to engineering lab report writing (relating to audience awareness, organizational structures, presentation/analysis/interpretation of lab data, use of primary and secondary sources, and document style design). We identify the prior writing knowledge and skills of the two distinct first-year writing-intensive course sites by investigating obvious points of productive transfer. This study provides a better understanding of how undergraduates use writing knowledge and skills earned from varying first-year writing-intensive contexts when writing their engineering labs. 

This exploratory study focuses on the use of ChatGPT, a generative artificial intelligence (GAI) tool, by undergraduate engineering students in lab report writing in the major. Literature addressing the impact of ChatGPT and AI on student writing suggests that such technologies can both support and limit students' composing and learning processes. Acknowledging the history of writing with technologies and writing as technology, the development of GAI warrants attention to pedagogical and ethical implications in writing-intensive engineering classes. This pilot study investigates how the use of ChatGPT impacts students’ lab writing outcomes in terms of rhetorical knowledge, critical thinking and composing, knowledge of conventions, and writing processes. A group of undergraduate volunteers (n= 7) used ChatGPT to revise their original engineering lab reports written without using ChatGPT. A comparative study was conducted between original lab report samples and revisions by directly assessing students’ lab reports in gateway engineering lab courses. A focus group was conducted to learn their experiences and perspectives on ChatGPT in the context of engineering lab report writing. Implementing ChatGPT in the revision writing process could result in improving engineering students’ lab report quality due to students’ enhanced lab report genre understanding. At the same time, the use of ChatGPT also leads students to provide false claims, incorrect lab procedures, or extremely broad statements, which are not valued in the engineering lab report genre. 

This study aims to identify the linguistic feature characteristics of multiple writing assignments completed by engineering undergraduates, including entry-level engineering laboratory reports and writing produced in non-engineering courses. We used Biber’s multidimensional analysis (MDA) method as the analysis tool for the student writing artifacts. MDA is a corpus-analysis methodology that utilizes language processing software to analyze text by parts of speech (e.g. nouns, verbs, prepositions, etc.). MDA typically identifies six “dimensions” of linguistic features that a text may perform in, and each dimension is rated along a continuum. The dimensions used in this study include Dimension 1: Informational vs involved, Dimension 3: Context dependence, Dimension 4: Overt persuasion, and Dimension 5: Abstract vs. non-abstract information. In AY 2019-2020, total of 97 student artifacts (N = 97) were collected. For this analysis, we grouped documents into similar assignment genres: research-papers (n = 45), technical reports and analyses (n = 7) and engineering laboratory reports (n = 35), with individual engineering students represented at least once in the laboratory report and once in another category. Findings showed that engineering lab reports are highly informational, minimally-persuasive, and used deferred elaboration. Students’ research papers in academic writing courses, conversely, were highly involved, highly persuasive, and featured more immediate elaboration on claims and data. The analyses above indicate that students are generally performing as expected in lab report writing in entry-level engineering lab classes, and that this performance is markedly different from their earlier academic writing courses, such as first-year-composition (FYC) and technical communication/writing, indicating that students are not merely “writing like engineers” from their first day at college. However, similarities in context dependence suggest that engineering students must still learn to modulate their languages in writing dramatically depending on the writing assignment. While some students show little growth from one context to another, others are able to change their register or other linguistic/structural features to meet the needs of their audience.