Dataset Information

Teaching Metabolism in Upper-Division Undergraduate Biochemistry Courses using Online Computational Systems and Dynamical Models Improves Student Performance.

ABSTRACT: Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of metabolic networks. However, multiple professional societies have recognized that most undergraduate biochemistry students acquire only a surface-level understanding of metabolism. We hypothesized that guiding students through interactive computer simulations of metabolic systems would increase their ability to recognize how individual interactions between components affect the behavior of a system under different conditions. The computer simulations were designed with an interactive activity (i.e., module) that used the predict-observe-explain model of instruction to guide students through a process in which they iteratively predict outcomes, test their predictions, modify the interactions of the system, and then retest the outcomes. We found that biochemistry students using modules performed better on metabolism questions compared with students who did not use the modules. The average learning gain was 8% with modules and 0% without modules, a small to medium effect size. We also confirmed that the modules did not create or reinforce a gender bias. Our modules provide instructors with a dynamic, systems-driven approach to help students learn about metabolic regulation and equip students with important cognitive skills, such as interpreting and analyzing simulation results, and technical skills, such as building and simulating computer-based models.

SUBMITTER: Booth CS

PROVIDER: S-EPMC8108505 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Student evaluations of teaching (SETs) have become a widely used tool for assessing teaching in higher education. However, numerous investigations have shown that SETs are subject to multiple biases, one of which is particularly relevant, namely, the area of knowledge to which the subject belongs. This article aims to replicate the article by Uttl & Smibert (2017, https://doi.org/10.7717/peerj.3299) in a different educational context to verify whether the negative bias toward instructors who teach quantitative courses found by the authors in the US also appears in the Spanish university system. The study was conducted at the Business and Law School of the Universidad Pontificia Comillas, a private Spanish university, using two different samples. First, we analyzed undergraduate courses using a sample of 80,667 SETs in which 2,885 classes (defined as a single semester-long course taught by an individual instructor to a specific group of students), 488 instructors, and 322 different courses were evaluated over a time period of four academic years (2016/2017-2019/2020). Second, in the same period, 16,083 SETs corresponding to master's degree courses were analyzed, which involved the study of 871 classes, 275 instructors, and 155 different courses. All the data included in the analysis were obtained from official university surveys developed by a team of professionals specialized in teaching quality responsible for ensuring the reliability of the information. At the degree level, the results show that despite the considerable cultural and temporal difference between the samples, the results are very similar to those obtained by Uttl & Smibert (2017, https://doi.org/10.7717/peerj.3299); i.e., professors teaching quantitative courses are far more likely to obtain worse SETs than instructors in other areas. There are hardly any differences at the master's degree level, regardless of whether nearly 75% of master's degree instructors also teach at the undergraduate level. This leads us to three different conclusions. (1) Evidence suggests that the reason for these differences is not due to faculty teaching quantitative courses being less effective than faculty teaching in some other fields. Our results indicate that the same instructor is evaluated very differently depending on whether he or she teaches at the undergraduate or master's level. (2) It is essential to avoid comparisons of SETs between different areas of knowledge, at least at the undergraduate level. (3) A significant change in the use and interpretation of SETs is imperative, or its replacement by other evaluation mechanisms should be considered. If this does not occur, it is possible that in the future, there will be an adverse selection effect among professors of quantitative methods; i.e., only the worst professionals in quantitative methods will opt for teaching since the good professionals will prefer other jobs.

Project description:Due to the COVID-19 pandemic, many universities struggle to engage students while implementing a distance-based teaching/learning approach and to provide hands-on activities to students enrolled in STEM classes. Implementing service-focused activities that can be conducted by the students remotely can overcome these struggles. The goals of this study were to 1) implement citizen science activities focused on water quality using three commercially available low-cost test strips (2:1, 5:1, and 16:1) while teaching four undergraduate engineering courses at the University of Mississippi (UM) during a pandemic event, and 2) evaluate the acceptability and validate the results obtained. Eighty-five undergraduate students (citizen scientists) and five research scientists (control group) collected two water samples (with triplicates) after receiving detailed step-by-step written guidelines and video tutorials. One hundred twenty tap water samples were collected from private households across Lafayette County and its surrounding counties and multiple buildings on campus. Five laboratory fortified blank (LBF) samples were implemented to validate the results. While the academic background of the participants did not impact the results (p > 0.05), the results obtained using the different test strips were statistically different (p < 0.05). In fact, results obtained using the 2:1 and the 5:1 test strips were close to the LFBs, while, except for the higher concentration of Total Alkalinity (40 mg/L CaCO3), results obtained using the 16:1 test strips were significantly different than the LFBs. Results (in terms of pH, Nitrate, and Total Chlorine) obtained by the citizen scientists using the 2:1 and 5:1 test strips were consistent with those reported in the annual drinking water quality reports from UM and municipalities included in the investigated region. Overall, this activity was well received by the students. Approximately 75% of them agreed that this hands-on activity was a positive experience while struggling to attend face-to-face classes.

Dataset Information

Teaching Metabolism in Upper-Division Undergraduate Biochemistry Courses using Online Computational Systems and Dynamical Models Improves Student Performance.

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