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1.     Rationale or motivation for the study 

 

1A. Committee on Underrepresented Groups and the Expansion of the Science and Engineering Workforce (US), Committee on Science, Engineering, and Public Policy (US), & National Research Council (US). Policy and Global Affairs. (2010). Expanding underrepresented minority participation: America's science and technology talent at the crossroads. National Academies Press. 

1B. Hrabowski, F. a. (2011). Boosting minorities in science. Science (New York, N.Y.), 331(6014), 125. doi:10.1126/science.1202388 

These references address the need for addressing issues of enrollment and retention of underrepresented minority (URM) students in STEM disciplines. Freeman A. Hrabowski, the author of this Science editorial (1A), was the Chair of the Committee that wrote the report (1B). In this report and in his editorial, Hrabowski warned that, to meet its STEM labor workforce objectives, the U.S. would need to multiply its STEM graduation levels for underrepresented minorities by 4. As of 2011, only 2 to 3% of URM students who were 24 years old had earned a first university degree in the natural sciences or engineering. The report also points to the fact that, URM have lower completion rates relative than other students. 

 

2. Haak, D. C., HilleRisLambers, J., Pitre, E., & Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science (New York, N.Y.), 332(6034), 1213–6. doi:10.1126/science.1204820 

 

This study demonstrates the impact of a highly structured, constructivist curriculum that uses active learning on decreasing the achievement gap in under-served and under-prepared students (including a majority of URM students). It makes the case that low URM retention and success rates described in references 1A and 1B could be at least partially reversed by creating more opportunities for students to practice (with feedback) higher-order thinking skills such as Bloom’s levels 3-6. 

 

3.A. Boggs, G. R. (2000). Growing Roles for Science Education in Community Colleges. Science, 3 September (329), 1151–1152. 

3. B. Fletcher, L. A., & Carter, V. C. (2010). The Important Role of Community Colleges in Undergraduate Biology Education, 9, 382–383. doi:10.1187/cbe.10 

 

In these features, the authors mention 2010 data from the American Association of Community Colleges which indicate that half of the students who receive a baccalaureate degree attend a community college at some point, and that a majority of African American and Hispanic undergraduate students study at these colleges. In addition, almost half of the students who graduate with a Bachelor’s degree in a STEM discipline attended a community college. This data helps establish the motivation for my study, which focuses on barriers to transfer for community college transfer in STEM disciplines.  

 

4. Johnson, M. L., & Sinatra, G. M. (2013). Use of task-value instructional inductions for facilitating engagement and conceptual change. Contemporary Educational Psychology, 38(1), 51–63. doi:10.1016/j.cedpsych.2012.09.003 

 

In this study, the authors focus on the affective characteristics of learning science. They look at how “hot” constructs, such as emotion and motivation, impact instruction-induced conceptual changes (learning) in science. In Dole and Sinatra’s Cognitive Reconstruction of Knowledge Model (CRKM), motivation instigates and sustains cognitive engagement (a learner’s motivated interaction with a task) which in turn provides the learner with the abilities to make connections and process information at a deep level, a step necessary to restructure existing conceptions and promote strong conceptual change. In this study, Johnson et al. look at the relationship between task value and conceptual change itself, not just academic performance. This study demonstrated that enhancing utility task values tends to promote engagement and more cognitive change (learning) than enhancing attainment task values, but that both were more effective than not enhancing task values at all. This study helps establish the importance of motivational constructs, such as task value, for effective acquisition of scientific knowledge and support our motivation for the study.  

 

5. Credé, M., & Phillips, L. A. (2011). A meta-analytic review of the Motivated Strategies for Learning Questionnaire. Learning and Individual Differences, 21(4), 337–346. doi:10.1016/j.lindif.2011.03.002 

 

The authors summarize the research literature on how metacognition, motivation and behavior relate to academic performance. Through this meta-analysis of studies using MSLQ in the context of college courses, the authors found that moderate to strong relationships were found between class grades and a few MSLQ constructs: self-efficacy, effort regulation (strongest), time and study environment management subscales. Effort regulation scores are at the level of known strong predictors of academic performance, such as prior academic performance or scores on admissions tests. Interestingly, they also found low correlations between academic performance and some specific learning strategies. One of their hypotheses for this lack of correlation is that students may not be assessed on higher-order thinking skills like critical thinking or elaboration in college, and therefore the use of elaborated learning strategies may not be useful for academic success. This study helps establish the relationship between learning strategies measured in the MSLQ survey and academic performance in college students. 

 

 

2.     Study Design  

 

6. Pintrich, P., Smith, D., Garcia, T., & McKeachie, W. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, Michigan: National Center for Research to Improve Post secondary Teaching and Learning. 


The primary instrument used in this study is the Motivated Strategies for Learning Questionnaire, or MSLQ, developed by Pintrich et al. in 1991 to measure undergraduate student’s motivation and use of learning strategies. The instrument, a self-report tool, was designed to be administered in class and was validated with different types of college student populations, including community college students. It uses a 7-point Likert Scale (1 being “not at all true of me” and 7 “very true of me”) and includes 81 items measuring 6 Motivation Scales (31 items) and 9 Learning Strategies Scales (50 items).  An individual’s score for each scale is constructed by taking the mean of all the items that make up that scale. For example, intrinsic goal orientation is made up of 4 items – the 4 items are summed up and the average is used to calculate the intrinsic goal orientation score (except in the case when there is a reverse coded item, in which case the actual score for this particular item is calculated by subtracting the original score to 8). Although the authors chose to provide feedback to students on 9 of their scales, we did not. The authors do not provide “norms” for the MSLQ and expect that the values will vary depending on the course. 

 

7. Salamonson, Y., Everett, B., Koch, J., Wilson, I., & Davidson, P. M. (2009). Learning strategies of first year nursing and medical students: a comparative study. International Journal of Nursing Studies, 46(12), 1541–7. doi:10.1016/j.ijnurstu.2009.05.010 

 

Just like us, the authors of this study used a comparative survey design to examine students’ motivation and learning strategies using part of the MSLQ survey in first year medical and nursing students, two groups who will later work alongside each other in an interprofessional education (IPE) setting. One interesting aspect in this study is that authors also looked at sociodemographic variables, such as paid employment time for these students, just like we did. They used basic statistics to compare differences between groups (Mann-Whitney U-test for continuous variables, which were not normally distributed, and chi-square for categorical variables) and found that average number of nursing students working in paid employment and the average time spent in that employment was much higher (up to 3 times higher for time worked) than for medical students, even for high performing nursing students (with a high GPA). They also found significant differences in some of the learning strategies and motivational constructs of nursing students. This study will be helpful for our statistical analysis and the interpretation of our data. 

 

 

3.     Methods for data collection and analysis  

 

8. Cole, J. S., Bergin, D. a., & Whittaker, T. a. (2008). Predicting student achievement for low stakes tests with effort and task value. Contemporary Educational Psychology, 33(4), 609–624. doi:10.1016/j.cedpsych.2007.10.002 

 

Cole et al. found that two out of three task values tested in this study of 1005 undergraduate students (usefulness and importance of a task, but not interest) predicted test-taking effort (self-report) and performance on four low-stake subject tests (the CollegeBASE or CBASE, a standard college-level general test covering English, Math, Social Studies). These data support the expectancy-value theory: Task value (equivalent to motivation in Johnson et al.) -> student effort (equivalent to engagement) -> student performance (equivalent to deep learning). However, when it came to Science, the model was less supported – although motivation impacted performance, it only did so partly through increased effort/engagement. In our study, we asked students to report their study time for their course – in this study, Cole et al. reported their effort on each test as a percentage scale. Although the authors indicate that the interest and usefulness items were created based on items included in the MSLQ, they do not provide enough information to create a strict correspondence between items – however, based on the description provided in the MSLQ, these do correspond to some of the “task value” items in the MSLQ.  

 

4.     Interpretation of results 

 

9. Dahl, T. I., Bals, M., & Turi, A. L. (2005). Are students’ beliefs about knowledge and learning associated with their reported use of learning strategies? The British Journal of Educational Psychology, 75(Pt 2), 257–73. doi:10.1348/000709905X25049 

 

Dahl et al. summarize the literature on epistemological beliefs (beliefs about knowledge and learning) and describe in depth the work by Schommer et al. that focuses on the relationship between epistemological beliefs and knowledge-related outcomes. Very much like Johnson et al., they focus here on the “work that learners engage in preceding their learning outcomes” and the process of knowledge acquisition. They used Pearson r correlation coefficients to analyze relationships between items of the Schommer Epistemological Questionnaire and of the MSLQ as well as full model regression analyses to test the predictive value of some beliefs on the reported use of learning strategies. They found that beliefs about how knowledge is organized have the greatest relationship with the use of rehearsal and organization strategies, while beliefs about how fixed learning have the greatest relationship with the selection of elaboration and critical thinking learning strategies.  

 

10. Bye, D., Pushkar, D., & Conway, M. (2007). Motivation, Interest, and Positive Affect in Traditional and Nontraditional Undergraduate Students. Adult Education Quarterly, 57(2), 141–158. doi:10.1177/0741713606294235 

 

The authors of this study asked whether different age groups of Canadian undergraduate students (18-21 for traditional students, vs. older than 27 for non-traditional students) showed different levels of motivation, intrinsic motivation and other traits. This paper gives an excellent over view of the body of research on adult learners and academic performance, motivation and learning strategies. Their demographic survey includes items that were similar to the survey I used, and included financial aid status, one possible source of stressors and their groups were about the same size as mine. They found that non-traditional (NT) students reported higher levels of intrinsic motivation than traditional (T) students, confirming prior results by Justice et al. They believe that the fact that the levels of extrinsic motivation are the same for NT and T may simply reflect the uniformity in expectations of the course itself. They found, however, that NT students reported a higher degree of need to enjoy the educational process to persist within the system than T students. Reinforcing intrinsic motivation in a classroom with T and NT students will result in more positive affect, and more resilience and persistence in academia.  

 

 

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