Abstract
The purpose of this study was to observe the relationship between students’ cognitive abilities and their performance in organic chemistry. We were interested in measuring whether some cognitive composites were more predictive than others on organic chemistry performance, whether group differences existed between males and females, and whether group differences existed between students with above and below average cognitive abilities. For Study 1 and Study 2, our participants included 48 and 60 sophomore organic chemistry students respectively. We used the Woodcock-Johnson Test of Cognitive Abilities-IV to measure cognitive composites. ACS organic chemistry exam scores and scores on an organic chemistry concept inventory were used to measure student performance. We ran a correlational analysis between the cognitive composites and organic chemistry scores, and t-tests for group comparisons. For Study 1, we found a significant moderate correlation between long-term retrieval and organic chemistry scores. For Study 2, we found a significant small to moderate correlation between comprehension knowledge and short-term working memory, with organic chemistry scores. We did not find any significant gender differences, except on comprehension knowledge. The differences between above average and below average cognitive abilities were only seen in relation to the concept inventory and not ACS exam scores.
1 Introduction
Understanding the cognitive profiles of college students pursuing science, technology, engineering, and math (STEM) fields is beneficial for two reasons: (a) it gives undergraduate professors an insight into how to improve their pedagogy; and (b) it can be used as a screening tool to assess if students’ cognitive profiles support their interest in and likelihood to succeed in certain STEM careers or not. One factor that drives students to choose STEM careers is a higher math ability compared to verbal ability (Wang, Eccles, & Kenny, 2013). It has been reported that a high-IQ mathematics and science talented group exhibited balanced cognitive development whereas the average-IQ mathematics and science talented group exhibited weakness in perceptual organization and working memory (Kuo, Liang, Tseng, & Gau, 2014). We present our findings in light of the following considerations while measuring cognitive profiles: (a) Though cognitive profiles impact career choices, they are shaped by broader socio-cultural contexts such as poverty, school quality, access to resources, gender and racial stereotypes etc. (Wang & Degol, 2013, 2016); (b) Cognitive profiles are also more malleable to change (Wang & Degol, 2016). We acknowledge these considerations as it gives us a broader perspective regarding the conceptualization of cognitive profiles for sophomore students and we are cognizant that we might see some variability if students are tested at a later date as they gain more proficiency in their majors. Our study addresses an important research gap in the field by presenting cognitive profiles of neurotypical college students pursuing organic chemistry. This interdisciplinary study is a convergence of our knowledge and expertise in psychology and chemistry. It is somewhat unconventional because the cognitive test that was utilized for the study is usually used as a formal diagnostic test to determine eligibility and support for students with disabilities. It is however normed for children and adults and can test cognitive profiles for both students with and without disabilities, though a lot of the research literature is primarily focused on the former.
Organic chemistry has been found to be notoriously difficult for students, and is often considered a gatekeeper course (Grove, Cooper, & Cox, 2012). Two semesters of organic chemistry are usually a requirement for students seeking admission to professional schools like medical school and dental school. As a result, there are many non-chemistry major students who are required to take organic chemistry. Therefore, understanding the challenges that students face in organic chemistry and predictors of student success in organic chemistry has been a big part of chemistry education research.
Researchers in the field of organic chemistry education research have employed both qualitative and quantitative methods to study various aspects of the cognitive domain (Anderson & Bodner, 2008; Bhattacharyya & Bodner, 2005; Ferguson & Bodner, 2008; Raker, Trate, Holme, & Murphy, 2013) and the affective domain (Steiner & Sullivan, 1984; O’Dwyer & Childs, 2017; Raker, Gibbons, & Cruz-Ramirez de Arellano, 2019). But there is a dearth of research in terms of cognitive profiles of students as predictors of student success in organic chemistry classes. The few studies done in this area are focused primarily on the role of visual-spatial reasoning on success in organic chemistry (Abraham, Varghese, & Tang, 2010; Hegarty, Stieff, & Dixon, 2013; Pribyl & Bodner, 1987; Stieff, Ryu, Dixon, & Hegarty, 2012).
It is argued that identifying meaningful spatial relationships within molecular structure is an important aspect of organic chemistry education (Habraken, 1996) and therefore the cognitive ability of visual-spatial reasoning is central to organic chemistry. But the effect of other cognitive abilities on success in organic chemistry, could also be of interest. This study seeks to expand our knowledge by creating more holistic cognitive profiles for students by correlating multiple cognitive composites with student performance in organic chemistry. The cognitive composites of comprehension-knowledge, fluid reasoning, cognitive processing speed, short-term working memory, long-term retrieval, and visual processing were used. Each cognitive composite consists of multiple subtests that help to capture it more accurately. By creating these cognitive profiles and comparing multiple subtests to performance in organic chemistry, we hope to shed light on how students utilize different cognitive abilities in addition to visual-spatial thinking when navigating through an organic chemistry class. Our understanding of cognitive profiles may give us insight into modifying instructional practices to meet students’ individual needs in the classroom and improve their educational outcomes in organic chemistry. Moreover, we hope that by establishing a link between different cognitive abilities and student performance in organic chemistry, we can also address a research gap in the literature which will inform other instructors about improving instructional practices in organic chemistry classes that tap into the same cognitive correlates.
2 Theoretical framework
The importance of the organic chemistry course in the undergraduate curriculum has led to interest in understanding the factors that affect student performance. The cognitive ability of organic chemistry students as a predictor of success in the course, is one of the most studied factors. Organic chemistry is a very visual science and students routinely encounter different 2-D and 3-D representations. Interpreting these representations and translating between representations is one of the biggest challenges that students face (Kozma & Russell, 1997). Kozma, Chin, Russell, and Marx (2000) used narrative analysis to conclude that representations are an inherent part of chemistry. Kozma and Russell (2005) highlighted five levels of representational competence and they reported that novice students did not have the fundamental knowledge needed to work with representations compared to experts. Another study (Bodner & Domin, 2000) explored the differences in how students’ problem-solve in organic chemistry. The results suggested that successful problem-solvers relied heavily on constructing more representations compared to unsuccessful problem-solvers who relied more on verbal descriptions.
Within the broader concept of representational competence, one of the cognitive skills that is most frequently studied is visual-spatial ability. One study compared two experimental groups that received 2-D and 3-D supplemental learning after conventional teaching with a control group that only received conventional teaching (Oke & Alam, 2010). The results suggest chemistry students that received 2-D and 3-D instruction, performed better in organic chemistry than the students that did not receive this type of instruction. The role of molecular representations in understanding stereochemistry has been studied and it has been reported that students with higher spatial abilities have higher achievement in learning stereochemistry (Pribyl & Bodner, 1987; Carter, LaRussa, & Bodner; 1987; Coleman & Gotch, 1998). The effect of three different methods of instruction namely, computer-based visualization software, ball and stick models, and two-dimensional perspective drawing, on understanding of stereochemistry was the subject of another study (Abraham et al., 2010). The results reported suggest that computer-visualization software is an effective tool and is superior to handheld models as it provides an avenue for students to link the 2-D representation to the 3-D one. It is also reported that depending on the activity, a combination of both types of models would be helpful.
Some studies have shown that students use visualization as a strategy for problem solving in organic chemistry in some cases, but then they rely on multiple other strategies involving heuristics when solving other problems. One study reported the use of think-aloud interviews with organic chemistry students to understand how much imagistic reasoning they use when problem-solving (Stieff, 2011). The results suggest that students use imagistic reasoning mainly when translating between various molecular representations, but when faced with more complex problems like the ones on classroom assessments, the students tend to depend more on self-generated inscriptions rather than visualization. Another study explored the use of other problem-solving strategies such as algorithms and heuristics to further understand the impact of visual-spatial reasoning (Stieff et al., 2012). The results indicate that students’ choice of problem-solving strategy is independent of visual-spatial ability and women employed strategies differently than men. A more recent study (Vlacholia et al., 2017), reported the development of the Visual Analytic Chemistry Task (VACT) and the performance of participants with various level of organic chemistry expertise on the VACT. The results suggest that the adoption of analytic strategies in organic chemistry is a difficult process and there is a systematic shift with acquisition of organic chemistry expertise. Furthermore, this study questions the role of visual-spatial reasoning in chemistry and suggests that more research needs to be done to investigate if the best approach to instruction would be a combination of both visual-spatial and analytic strategies as suggested in the literature (Stull & Hegarty, 2016).
The effect of other cognitive skills on performance in organic chemistry have not been explored as much as visual-spatial reasoning. Some studies have focused on students’ reasoning strategies when solving organic chemistry problems (Kraft, Strickland, & Bhattacharyya, 2010; Christian & Talanquer, 2012; Cruz-Ramirez de Arellano & Towns, 2014). These studies have shown that students tend to use minimal reasoning strategies and rely more on memorization. It has been argued that memorization is one approach to learning that has been employed by both undergraduate organic chemistry students (Grove & Bretz, 2010; Grove et al., 2012) and graduate students as well (Bhattacharyya & Bodner, 2005). Therefore, the effect of various cognitive skills in addition to visual-spatial ability on performance in organic chemistry, could address a research gap in the literature and add to the existing information on the main cognitive skills that are employed by organic chemistry students.
Differences in cognitive abilities based on gender have been studied extensively in STEM fields. A large study explored gender difference in eight areas including achievement, and they reported that female students have greater verbal ability while male students have greater visual-spatial ability (Maccoby & Jacklin, 1974). A more recent study conducted by Educational Testing Services (ETS), reported that verbal ability still favors females (Cole, 1997). Though it has been reported that males have a higher visual-spatial ability, there is no conclusive evidence to show that there is a relationship between this ability and performance in the physical sciences (Halpern, 1997). The presence of differences between the cognitive abilities of men and women and its impact on performance in organic chemistry, would be worth exploring.
Based on the theoretical background and research gap that we have found in the field, these are the following research questions for the study:
RQ 1. (a) Study 1: Are cognitive abilities of visual processing, long-term retrieval, and fluid reasoning predictive of student performance in organic chemistry? (b) Study 2: Are cognitive abilities of comprehension knowledge, short-term working memory and cognitive processing speed predictive of student performance in organic chemistry?
RQ 2. Are there any group differences between students with above average and below average cognitive abilities in relation to their performance in organic chemistry?
RQ 3. Are there any group differences between males and females with respect to their cognitive abilities in relation to their performance in organic chemistry?
3 Methods
3.1 Participants
Our participants for Study 1 included 48 students and our participants for Study 2 included 60 students. Students in Study 1 and 2 attended a public four-year university in the Midwest region of the U.S.A. and were enrolled in a second-semester undergraduate organic chemistry class in Spring 2022 and Fall 2022 respectively. Only 5 % of the population (3 out of 60 students) participated in both studies. The mean age of participants for Study 1 was 23 years and 3 months and for Study 2 was 22 years and 8 months.
3.2 Measures
3.2.1 Woodcock-Johnson tests of cognitive abilities-IV (WJ-IV COG)
The WJ-IV COG (Schrank, Mather, & McGrew, 2014) was normed on over 7000 individuals ranging from 2 to over 90 years, including college and university undergraduate and graduate students. The demographic and community characteristics closely match those of the general population in the U.S.A. The WJ-IV is the most widely used cognitive test for educational, clinical or research purposes from the preschool to geriatric level (Schrank et al., 2014). The scores obtained on the WJ-IV COG are often used to assess students’ cognitive strengths and weaknesses, diagnose specific abilities and disabilities, and produce the most relevant data for educational and vocational planning (Schrank et al., 2014).
The WJ-IV COG test consists of two index scores, namely general intellectual ability and brief intellectual ability, and seven composite scores. We did not calculate the index scores that related to an overall IQ, but rather focused on the individual cognitive composites that would give us a better understanding of the relation of these skills to organic chemistry scores. Students across both studies were administered six out of the seven composites on the WJ-IV COG test, except auditory processing, because the test measured phonological processing as a precursor to learning to read, which was at a very basic level for our sample of college students. For students in Study 1, we administered three composites, namely fluid reasoning, long-term retrieval and visual processing. For students in Study 2, we administered three composites, namely short-term working memory, comprehension-knowledge, and cognitive processing speed. Each composite consists of multiple subtests that are time consuming to administer. The composites were split up over two studies to ease the burden on the participants.
The composites measured in Study 1 are described below:
Fluid Reasoning: includes a broad ability to reason, form concepts and solve problems using unfamiliar information or novel procedures.
Long-Term Retrieval: is the ability to store information and fluently retrieve it later in the process of thinking.
Visual Processing: is the ability to perceive, analyze, synthesize, and think with visual patterns, including the ability to store and recall visual representations.
The composites measured in Study 2 are described below:
Comprehension Knowledge: or crystallized intelligence, includes the breadth and depth of a person’s acquired knowledge, the ability to communicate one’s knowledge and the ability to reason using previously learned experiences and procedures.
Cognitive Processing Speed: is the ability to quickly perform both simple and complex cognitive tasks, particularly when measured under pressure to sustain controlled attention and concentration.
Short-Term Working Memory: is the ability to apprehend and hold information in immediate awareness and then use or manipulate it to carry out a goal. Information is typically retained for a few seconds before it is lost or transformed. It includes both holding information as well as cognitive efficiency of attentional control during that process.
3.2.2 Fundamental concepts for organic reaction mechanisms inventory (FC-ORMI)
The FC-ORMI is a 26-item multiple-choice instrument designed to assess student knowledge of fundamental concepts for understanding organic reaction mechanisms (Nedungadi, Mosher, Paek, Hyslop, & Brown, 2021). The concepts include a mixture of general chemistry concepts and fundamental organic chemistry concepts. The items on the FC-ORMI utilize student alternate conceptions as distractors or incorrect responses. The psychometric analysis of the FC-ORMI has been established (Nedungadi et al., 2021) and it has shown that the instrument is a reliable measure for assessing student knowledge of fundamental concepts for understanding organic reaction mechanisms.
3.2.3 American Chemical Society (ACS) organic chemistry exam
The final assessment was the full year ACS organic chemistry exam which was administered as a final exam. The ACS organic chemistry exam is a nationally normed exam containing 70 questions under different content areas that are typically covered during the two-semester organic chemistry sequence. The raw score out of 70 was utilized for data analysis.
3.3 Data collection procedures
Institutional Review Board approval was obtained before data collection at the university where the study was conducted. All participants were required to sign an informed consent document outlining the benefits and risks of participating in the study. Data collection for Study 1 was conducted over the Spring 2022 semester and data collection for Study 2 was conducted over the Fall 2022 semester. We collected data over two semesters because of the time commitment required to individually administer tests of cognitive abilities. On the WJ-IV COG, each composite comprised of two to three subtests. Individual subtests were administered and scored by two trained researchers. The total individual administration time was 45–60 min during both the Spring 2022 and Fall 2022 semesters respectively. Reports with standard scores were generated using the WJ Online Scoring system.
For Study 1, the FC-ORMI was administered in Spring 2022 and for Study 2, the FC-ORMI was administered in Fall 2022. Both administrations took place during the last week of the second-semester organic chemistry class. The data were collected during the regular class period. Students were asked to record their answers on bubble sheets, and they took approximately 20 min to complete the assessment.
The full year ACS organic chemistry final exam was given at the end of the Spring 2022 and Fall 2022 semesters. The same version of the final exam was used in both semesters. Students were given 110 min to complete the exam and they recorded their answers on bubble sheets.
3.4 Data analysis
The main aim of the current study was to explore the relationship between different cognitive profiles and performance in organic chemistry. Research Question 1 was aimed at understanding the predictive nature of cognitive abilities on organic chemistry scores, and was answered using Pearson’s correlation coefficient values. Research Questions 2 and 3 were aimed at understanding the group differences between above average and below average cognitive abilities and gender differences, and was answered using independent t-tests. All data analysis was conducted using the statistical software Stata (StataCorp, 2021).
4 Results and discussion
4.1 Correlations between cognitive abilities and organic chemistry performance
Our first research question states: (a) Study 1: Are cognitive abilities of visual processing, long-term retrieval, and fluid reasoning predictive of student performance in organic chemistry? (b) Study 2: Are cognitive abilities of comprehension knowledge, short-term working memory, and cognitive processing speed predictive of student performance in organic chemistry?
Table 1 shows the correlation matrix for the cognitive composites measured in Study 1. There were no significant correlations between ACS exam scores and the cognitive abilities measured. We found a moderate positive correlation between long-term retrieval and the FC-ORMI scores (r(47) = 0.40, p < 0.01), but no significant correlations between other cognitive abilities measured and FC-ORMI scores. The items on the FC-ORMI are related to both general chemistry and fundamental organic chemistry concepts that are covered within the first month of a first-semester organic chemistry class. This correlation between long-term retrieval and performance on these fundamental organic chemistry concepts suggests that organic chemistry students tend to resort to their stored memory even when it comes to applying concepts that they have been exposed to repeatedly. These results are consistent with what has been reported in the literature on organic chemistry students’ utilization of rote memorization (Grove & Bretz, 2010; Grove et al., 2012; Bhattacharyya & Bodner, 2005).
Correlation matrix for visual processing, long-term retrieval, fluid reasoning, ACS exam scores, and FC-ORMI scores.
Visual processing | Long-term retrieval | Fluid reasoning | ACS scores | FC-ORMI scores | |
---|---|---|---|---|---|
Visual processing | 1 | ||||
Long-term retrieval | 0.65*** (0.000) | 1 | |||
Fluid reasoning | 0.51*** (0.0002) | 0.54*** (0.0001) | 1 | ||
ACS exam scores | 0.14 (0.3367) | 0.21 (0.16) | 0.22 (0.13) | 1 | |
FC-ORMI scores | 0.16 (0.2650) | 0.40** (0.0045) | 0.25 (0.0793) | 0.61*** (0.0000) | 1 |
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*p-value <0.05; **p-value <0.01; ***p-value <0.001.
Table 2 depicts the correlation matrix for the cognitive composites measured in Study 2. We found a small positive correlation between comprehension knowledge and ACS exam scores (r(59) = 0.34, p < 0.01) and a moderate positive correlation between comprehension knowledge and FC-ORMI scores (r(59) = 0.45, p < 0.001) respectively. These results suggest that students are utilizing previously gained chemistry knowledge and their reasoning abilities using this knowledge, as they progress through their chemistry classes. This effect is slightly more pronounced on the FC-ORMI than the cumulative ACS exam, but the results highlight the inherent cumulative nature of chemistry. The results also suggest the importance of a good foundational understanding of chemistry concepts to performance in organic chemistry.
Correlation matrix for comprehension knowledge, short-term working memory, cognitive processing speed, ACS exam scores, and FC-ORMI scores.
Comprehension knowledge | Short-term working memory | Cognitive processing speed | ACS scores | FC-ORMI scores | |
---|---|---|---|---|---|
Comprehension knowledge | 1 | ||||
Short-term working memory | 0.24 (0.0670) | 1 | |||
Cognitive processing speed | 0.17 (0.2198) | 0.35** (0.0060) | 1 | ||
ACS scores | 0.34** (0.0072) | 0.36** (0.0042) | 0.12 (0.3614) | 1 | |
FC-ORMI scores | 0.45*** (0.0003) | 0.30** (0.0198) | 0.16 (0.1967) | 0.63*** (0.0000) | 1 |
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*p-value <0.05; **p-value <0.01; ***p-value <0.001.
We also found a small positive correlation between short-term working memory and both ACS exam scores (r(59) = 0.36, p < 0.01) and FC-ORMI scores (r(59) = 0.30, p < 0.01) respectively. These results indicate that students tend to rely on information that they have obtained more recently, and this impacts their performance in organic chemistry although they might not be relying on this as much as long-term memory.
4.2 Relationship between cognitive ability levels and organic chemistry performance
Our second research question states: Are there any group differences between students with above average and below average cognitive abilities in relation to their performance in organic chemistry?
Table 3 depicts the results of an independent t-test that was conducted to measure group differences between students with above average and below average cognitive abilities. We created the two groups by using the cut-off for standard scores that are fitted to a normal curve on the norm-referenced cognitive abilities test, with a mean of 100 and SD of 15. Our below average group constituted students whose scores were at or below 100 and our above average group constituted students whose scores were above 100.
Independent t-test results showing differences between students with above average and below average cognitive abilities across study 1 and study 2.
Mean (SD) | t-statistic | p-value | Mean diff (effect size) | |||
---|---|---|---|---|---|---|
Study 1 | Fluid reasoning | Above average | 115.85 (9.13) | 9.43 | 0.00*** | 22.01 (d = 2.41) |
Below average | 93.84 (6.31) | |||||
ACS/Above | 40.85 (8.43) | 2.22 | 0.04* | 5.62 (d = 0.66) | ||
ACS/Below | 35.23 (7.55) | |||||
FC-ORMI/Above | 18.65 (3.97) | 3.00 | 0.01** | 3.27 (d = 0.82) | ||
FC-ORMI/Below | 15.38 (3.09) | |||||
Long-term retrieval | Above average | 114.56 (2.08) | 8.52 | 0.00*** | 25.79 (d = 11.77) | |
Below average | 88.77 (2.19) | |||||
ACS/Above | 40.5 (1.63) | 1.28 | 0.21 | 3.12 (d = 1.75) | ||
ACS/Below | 37.38 (1.78) | |||||
FC-ORMI/Above | 18.66 (3.88) | 2.07 | 0.04* | 2.39 (d = 0.62) | ||
FC-ORMI/Below | 16.27 (3.83) | |||||
Visual processing | Above average | 113.28 (7.61) | 8.05 | 0.00*** | 23.38 (d = 2.69) | |
Below average | 89.90 (8.70) | |||||
ACS/Above | 39.35 (8.87) | 0.03 | 0.97 | 0.08 (d = 0.00) | ||
ACS/Below | 39.27 (7.54) | |||||
FC-ORMI/Above | 17.81 (3.99) | 0.12 | 0.90 | 0.18 (d = 0.04) | ||
FC-ORMI/Below | 17.63 (4.20) | |||||
Study 2 | Comprehension knowledge | Above average | 106.76 (5.73) | 8.74 | 0.00*** | 14.69 (d = 2.38) |
Below average | 92.07 (6.17) | |||||
ACS/Above | 37.17 (7.41) | 1.16 | 0.25 | 2.75 (d = 0.27) | ||
ACS/Below | 34.42 (10.16) | |||||
FC-ORMI/Above | 19.17 (3.04) | 2.48 | 0.01** | 2.34 (d = 0.61) | ||
FC-ORMI/Below | 16.83 (3.82) | |||||
Short-term working memory | Above average | 111.38 (7.22) | 11.50 | 0.00*** | 19.67 (d = 2.72) | |
Below average | 91.71 (5.95) | |||||
ACS/Above | 37.42 (9.86) | 2.39 | 0.02* | 5.55 (d = 0.56) | ||
ACS/Below | 31.87 (7.97) | |||||
FC-ORMI/Above | 18.33 (3.74) | 2.20 | 0.03* | 2.08 (d = 0.55) | ||
FC-ORMI/Below | 16.25 (3.47) | |||||
Cognitive processing speed | Above average | 110.37 (7.05) | 10.13 | 0.00*** | 15.97 (d = 2.26) | |
Below average | 94.4 (4.55) | |||||
ACS/Above | 35.86 (8.05) | 0.75 | 0.47 | 2.64 (d = 0.20) | ||
ACS/Below | 33.22 (13.02) | |||||
FC-ORMI/Above | 17.88 (3.43) | 1.22 | 0.24 | 1.55 (d = 0.34) | ||
FC-ORMI/Below | 16.33 (4.51) |
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*p-value <0.05; **p-value <0.01; ***p-value <0.001.
For Study 1, we found a statistically significant difference between students who scored below average and above average on all three cognitive abilities, namely fluid reasoning, long-term retrieval, and visual processing. The effect sizes were moderate to high across all three composites. Whereas this difference remained statistically significant on ACS exam scores and FC-ORMI scores for the fluid reasoning composite; it was not significant for both ACS exam scores and FC-ORMI scores on the visual processing composite; and only significant for the FC-ORMI scores and not for ACS exam scores on the long-term retrieval composite. The effect sizes were moderate to high on both the ACS and FC-ORMI groups on the fluid reasoning and long-term retrieval composites, but were low for both groups on the visual processing composite. These results indicate that a greater ability to engage in broad reasoning impacts performance in organic chemistry. The visual-spatial ability does not impact performance in organic chemistry significantly. Students with different levels of visual processing ability, tend to perform the same on an organic chemistry conceptual test and on an organic chemistry final exam. The FC-ORMI scores are more impacted by long-term retrieval given that it is a conceptual test designed to assess student understanding of fundamental general chemistry and organic chemistry concepts that they might encounter very early on in organic chemistry.
For Study 2, we found a statistically significant difference between students who scored below average and above average on all three cognitive abilities, namely comprehension knowledge, short-term working memory and cognitive processing speed. The effect sizes were moderate to high across all three composites. Whereas this difference remained statistically significant on ACS exam scores and FC-ORMI scores for the short-term working memory composite; it was not significant for both ACS exam scores and FC-ORMI scores on the cognitive processing speed composite; and only significant for the FC-ORMI scores and not for ACS exam scores on the comprehension knowledge composite. On comprehension knowledge, the effect size was moderate for the FC-ORMI group and small for the ACS group. On short-term working memory and cognitive processing speed, the effect sizes were moderate and small on both FC-ORMI and ACS groups respectively. These results further indicate that short-term working memory is a cognitive ability that directly impacts performance in organic chemistry and students tend to rely on their ability to recall information that was recently presented to them. The results are also consistent with the results from the correlational studies reported above where comprehension knowledge has a more significant effect on the FC-ORMI scores which further demonstrates the importance of foundational chemistry knowledge.
4.3 Relationship between cognitive abilities and organic chemistry performance by gender
Our third research question states: Are there any group differences between males and females with respect to their cognitive abilities in relation to their performance in organic chemistry?
Table 4 displays the results from an independent t-test to measure group differences between males and females in the study. For Study 1, we found no significant gender differences on fluid reasoning, long-term retrieval, and visual processing, despite females scoring slightly better than males on these cognitive abilities. This corresponded to the small effect sizes that were recorded across the composites. This trend continued for the ACS exam scores and FC-ORMI scores, despite females scoring slightly better than males on organic chemistry tests.
Independent t-test results showing gender differences across study 1 and study 2.
Mean | SD | t-statistic | p-value | Mean diff (effect size) | |||
---|---|---|---|---|---|---|---|
Study 1 | Fluid reasoning | Female | 110.71 | 13.17 | 0.51 | 0.61 | 1.96 (d = 0.14) |
Male | 108.75 | 12.92 | |||||
Long-term retrieval | Female | 107.00 | 17.70 | 1.08 | 0.28 | 5.05 (d = 0.28) | |
Male | 101.95 | 14.47 | |||||
Visual processing | Female | 109.21 | 13.15 | 0.84 | 0.40 | 3.06 (d = 0.23) | |
Male | 106.15 | 11.91 | |||||
ACS | Female | 39.67 | 8.46 | 0.33 | 0.74 | 0.82 (d = 0.09) | |
Male | 38.85 | 8.77 | |||||
FC-ORMI | Female | 18.32 | 4.11 | 1.15 | 0.26 | 1.32 (d = 0.32) | |
Male | 17.00 | 3.81 | |||||
Study 2 | Comprehension knowledge | Female | 93.66 | 9.23 | 2.29 | 0.03* | 5.14 (d = 0.55) |
Male | 98.80 | 8.08 | |||||
Short-term working memory | Female | 101.73 | 11.28 | 1.17 | 0.24 | 3.57 (d = 0.29) | |
Male | 105.30 | 12.22 | |||||
Cognitive processing speed | Female | 105.80 | 8.19 | 0.47 | 0.63 | 1.16 (d = 0.12) | |
Male | 106.96 | 10.80 | |||||
ACS | Female | 33.96 | 9.09 | 1.01 | 0.32 | 2.47 (d = 0.27) | |
Male | 36.43 | 1.80 | |||||
FC-ORMI | Female | 17.63 | 3.99 | 0.27 | 0.78 | 0.27 (d = 0.06) | |
Male | 17.36 | 3.55 |
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*p-value <0.05; **p-value <0.01; ***p-value <0.001.
For Study 2, we found a significant difference on comprehension knowledge (t(59) = 2.29, p < 0.05) with males (M = 98.8, SD = 8.08) scoring higher than females (M = 93.66, SD = 9.23). We found no significant gender differences on short-term working memory and cognitive processing speed, despite males scoring better than females on these cognitive abilities. This corresponded with the moderate effect size we found for comprehension knowledge, and the small effect sizes we found on the other two composites. This trend continued for the ACS exam scores and the FC-ORMI scores, despite males scoring better than females on these organic chemistry tests.
5 Conclusions
The aim of this research study was to explore the impact of cognitive abilities of sophomore undergraduate organic chemistry students on their performance in organic chemistry. The research study was divided into Study 1 which explored three different cognitive composites and Study 2 which explored three different cognitive composites. The WJ-IV COG was used as a measure of cognitive abilities. The FC-ORMI scores and ACS exam scores were used as measures of performance in organic chemistry. A correlation study was conducted between the measures for cognitive ability and the measures for organic chemistry performance. Study 1 results indicated a moderate positive correlation between long-term retrieval and FC-ORMI scores. Study 2 results indicated a small positive correlation between comprehension knowledge and short-term working memory with both FC-ORMI scores and ACS exam scores. The results suggest that organic chemistry students tend to rely on both long-term and short-term memories. Additionally, prior chemistry knowledge could impact student performance in organic chemistry, which highlights the importance of foundational chemistry concepts.
The relationship between cognitive ability levels and organic chemistry performance was also explored in both studies. The results from Study 1 indicate that fluid reasoning directly impacts performance in organic chemistry whereas visual-spatial ability has no significant impact. Long-term retrieval has more of an impact on FC-ORMI scores where items are related to foundational chemistry concepts that students encounter very early on in organic chemistry. The results from Study 2 indicate that short-term working memory impacts performance in organic chemistry but cognitive processing speed has no significant impact. Comprehension knowledge has more of an impact on FC-ORMI scores which further suggests the importance of a strong foundation in chemistry concepts for improved performance in organic chemistry.
Additionally, differences in cognitive abilities based on gender were explored. There were no significant differences in cognitive abilities based on gender in Study 1 and in Study 2 only comprehension-knowledge showed a statistical difference between males and females. There were no significant differences in organic chemistry performance based on gender in both studies.
6 Implications for teaching
The results from both Studies 1 and 2 could have major implications for teaching. Organic chemistry performance is related significantly to both long-term and short-term memories, and fluid reasoning. Students tend to utilize memorization and reasoning skills for both formative assessments like concept inventories and summative assessments like final exams. One of the reasons why organic chemistry is challenging is because students are expected to make fluid transitions from a conceptual understanding when drawing reaction mechanisms to memorization when learning nomenclature rules and remembering certain reagents. Organic chemistry instructors should make instructional modifications to cater to both types of cognitive abilities. One way in which instructors can do this is to make sure their assessments contain questions that require the utilization of these different cognitive abilities.
Some cognitive skills impact both FC-ORMI scores and ACS exams scores but there are some cognitive abilities that only impacted FC-ORMI scores. Long-term retrieval and comprehension knowledge impact the FC-ORMI scores more significantly. These results indicate the importance of utilizing both formative and summative assessments in an organic chemistry class. For example, the concept of acids and bases is widely used in the study of organic chemistry. Instructors could used formative assessments like concept inventories to gauge student understanding of specific aspects of acids and bases like strength of acids related to structure. The cognitive skill of comprehension knowledge could impact student understanding of this content. Instructors can address commonly held alternate conceptions before assessing students using summative assessments like final exams. The items related to acids and bases on a summative assessment could involve application questions like drawing the mechanism for a proton transfer step. The performance on such items would need a thorough understanding of the basic concept. The cognitive skills employed and hence performance on a formative assessment like a concept inventory could give organic chemistry instructors information on how well students are utilizing foundational chemistry concepts. The FC-ORMI is one such concept inventory that can be used to assess student understanding of fundamental chemistry concepts important for understanding organic reaction mechanisms. The psychometric properties of the FC-ORMI have been previously reported and the instrument is fully accessible for organic chemistry instructors to utilize in their classes (Nedungadi et al., 2021). Instructors could help their students by doing a thorough review of fundamental chemistry concepts in the beginning of an organic chemistry class. Additionally, instructors could continuously relate back to these fundamental chemistry concepts when teaching organic chemistry.
7 Limitations
The data in both Study 1 and Study 2 were collected at one university in the Midwest region of the U.S.A and therefore, more data would have to be collected to make the results generalizable. The cognitive tests must be administered individually, they are time consuming, and the researcher has to be trained to administer the tests which is why data collection was restricted to one university.
Acknowledgments
The authors would like to thank all the participants who devoted a significant amount of their time to take part in this research study.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- Cognitive discourse during a group quiz activity in a blended learning organic chemistry course
- Impact of cognitive abilities on performance in organic chemistry
- A safe-at-home benzoin condensation from imitation almond extract
- Determination of alkalinity in the water sample: a theoretical approach
- Special Issue Paper
- Learning with a purpose: a metals chemistry course centered on objects conservation
- Research Articles
- The reflection invariance problems in stereochemical nomenclature for absolute configuration
- The effect of context-based close packing supported with the 3D-virtual model of crystals structure on students’ achievement and attitude
- Special Issue Papers
- The rise and fall of the phlogiston theory: a tool to explain the use of models in science education
- Conservation Science Education Online (CSEO) – A heritage science resource
- “I don’t know, ask the chemists – I think it’s kind of a consensus among them” – Information practice in a problem-based beginner lab