(733h) Quantitative Analysis Of Motivation, Learning Strategies and Conceptual Understanding | AIChE

(733h) Quantitative Analysis Of Motivation, Learning Strategies and Conceptual Understanding


McCord, R. - Presenter, Virginia Tech
Matusovich, H. M., Virginia Tech

Quantitative Analysis on Motivation, Learning Strategies and Conceptual Understanding


Recent studies have shown that graduates of engineering programs are entering the workforce while still holding onto robust misconceptions in areas such as statics, electricity and magnetism, and thermodynamics (Streveler, Litzinger, Miller, & Steif,2008). While significant research has focused on the assessment of conceptual understanding and how to identify and repair misconceptions, little research has focused on the intentional ways that students engage in learning or the effects of this engagement on conceptual change. This quantitative analysis portion is part of a larger overall project, situated in a framework of intentional conceptual change that looks at the effect of student motivation and learning strategy choice on conceptual change in thermodynamics. Our three-phased study will identify the motivational factors and learning strategies students use that affect conceptual change, explore those factors more in depth through real-time data collection in the thermodynamics classroom, and then finally test interventions developed based on the factors identified.

This analysis focuses on the identification of relevant motivation and learning strategies factors through survey data collection and follow-up interviews. We developed a survey instrument to measure motivational factors and learning strategies that affect conceptual change drawing on existing scales such as the Motivated Strategies of Learning Questionnaire (Pintrich, Smith, Garcia, & McKeachie,1991), the Self-Regulated Learning Inventory (Lindner, Harris, & Gordon,1996), and the Thermal and Transport Concept Inventory (Streveler et al.,2011).

Literature Review

A critical challenge facing engineering education developing engineers with the technical expertise needed to lead innovation. In order to meet this challenge, we must first understand how students learn in order to create learning environments that support the development of deep, conceptual understanding. Research has shown that students enter into undergraduate programs with misconceptions about how the world works. We also know that students are graduating from undergraduate institutions still holding on to robust misconceptions in areas such as physics, electricity and thermodynamics (Streveler et al.,2008). To better understand how to correct misconceptions, we need to first understand how students engage in the process of conceptual understanding.

Hot cognition,” which merges traditional cognitive approaches with learner’s intentionality – i.e. why and how they choose to engage in learning – provides a framework that can be used to better understand student engagement in conceptual understanding (Boyle, Pintrich, & Marx,1993). This framework is different from “cold” approaches in that it considers not only knowledge structures, but adds intentionality, recognizing that learners make choices about their engagement in learning and their selection of learning strategies. We know little about students’ motivation and their corresponding learning approaches and the connection to conceptual understanding in engineering classrooms (Streveler et al.,2008).

Linnenbrink and Pintrich (2003) proposed a framework to explain how hot conceptual change occurs. The core precept of their model is that motivation for conceptual change determines how the learner will approach the task of learning. This model starts with a primary categorization of students’ motivation under Achievement Goals then considers Other Motivational Beliefs, such as self-efficacy and interest, and the associated Learning Strategies as contributing to Intentional Conceptual Change. This research study uses the framework posed by Linnenbrink and Pintrich to inform the development of instruments to measure motivation, learning strategies and conceptual change.


To better understand how students engage in conceptual learning, this first phase of our study answers the  research question: What are the intentionality factors (i.e., student motivation and learning strategy choice) and relationships among these factors that lead to successful conceptual change? (RQ 1)

To answer this question, a survey instrument was developed using existing instruments to measure motivation constructs, learning strategies and conceptual understanding. The motivation constructs considered for this survey instrument included extrinsic and intrinsic motivation, interest, attainment value, cost, identification with academics, self-efficacy and instrumentality. The learning strategies section of the survey consisted of elements from the Motivated Strategies of Learning Questionnaire (Pintrich et al.,1991), the Self-Regulated Learning Inventory (Lindner et al.,1996) and several questions designed by the researchers. The conceptual understanding portion of the survey consisted of questions from the Thermal and Transport Concept Inventory (Streveler et al.,2011). A detailed discussion of the survey development can be found here (McCord & Matusovich,2013). The survey was distributed to universities and colleges around the US by contacting thermodynamics professors and asking them to send the link to their students. In all, eleven universities from around the United States have participated in the survey.


A total of 81 student participants have completed the entire survey. While no significant correlations have been found linking specific motivational or learning strategies profiles to conceptual understanding, several significant correlations have been found between different motivational profiles and different choices for learning strategies that students engage in. These correlations are shown in Table 1, which show the Pearson correlations between the motivational factors and the learning strategies. For example, positive correlations have been found between the motivation constructs of interest and intrinsic motivation and engagement in learning strategies such as self-consequences, self-evaluation and monitoring. We have also found correlations between the learning strategy of seeking help from a teacher and motivational constructs such as interest, attainment value, self-efficacy and endogenous instrumentation. A preview of the correlation analysis conducted is shown in Table 1. 

Table 1: Correlations between Motivation Constructs and Learning Strategies

Learning Strategies

Time and Study Environment

Keeping Records




Setting Goals


Seeking Help from Teachers

Seeking Help from Peers








































Attainment Value
























Identification with Academics
























Endogenous Instrumentation












Exogenous Instrumentation












**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed)


Discussion and Conclusions

We found significant connections between a student’s motivational profile and the learning strategies that they choose to engage in when learning thermodynamics.  This is an important finding because it tells us that students motivational beliefs are directly tied to the actions that they engage in to learn content.  Understanding these connections may provide valuable information on how develop pedagogical interventions based on students’ motivational profiles.  

In the first round of data collection, we did not find significant ties between motivation, learning strategies and conceptual understanding. While this result goes against our initial hypothesis that there would be a connection, there may be methodological explanations as to why no connection was found. One possibility is that the survey questions were directed towards asking students about their experiences in their thermodynamics class. While we wanted to focus to be on the thermodynamics class, it may be difficult for students to relate their responses to thermodynamics as a whole. For example, questions asked students about their interest in thermodynamics yet concept inventory questions about specific content such as entropy or internal energy. It may be more appropriate to ask students about their interest in specific topics covered in thermodynamics. This may be more easily done in the context of interviews than through survey data collection.


Boyle, R. A., Pintrich, P. R., & Marx, R. W. (1993). Beyond Cold Conceptual Change: The Role of Motivational Beliefs and Classroom Contextual Factors in the Process of Conceptual Change. Review of educational research, 63(2), 167-199.

Lindner, R. W., Harris, B. R., & Gordon, W. I. (1996). The Design and Development of the "Self-Regulated Learning Inventory": A Status Report: Western Illinois University.

Linnenbrink, E. A., & Pintrich, P. R. (2003). Achievement Goals and Intentional Conceptual Change. In G. M. Sinatra & P. R. Pintrich (Eds.), Intentional Conceptual Change (pp. 347-374). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

McCord, R., & Matusovich, H. M. (2013). Developing an Instrument to Measure Motivation, Learning Strategies and Conceptual Change. Paper presented at the 120th ASEE Annual Conference & Exposition, Atlanta, GA.

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A Manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor, Mich.; [Washington, DC]: University of Michigan ; U.S. Dept. of Education, Office of Educational Research and Improvement, Educational Resources Information Center.

Streveler, R. A., Litzinger, T. A., Miller, R. L., & Steif, P. S. (2008). Learning Conceptual Knowledge in the Engineering Sciences: Overview and Future Research Directions. [Article]. Journal of Engineering Education, 97(3), 279-294.

Streveler, R. A., Miller, R. L., Santiago-Roman, A. I., Nelson, M. A., Geist, M. R., & Olds, B. M. (2011). Rigorous Methodology for Concept Inventory Development: Using the 'Assessment Triangle' to Develop and Test the Thermal and Transport Science Concept Inventory (TTCI). The International journal of engineering education, 27(5), 968-984.



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