Tag Archives: CALE

Pluralistic ignorance

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This semester I am teaching an introductory course in Thermodynamics to undergraduate students using a blended learning approach [see ‘Blended learning environments‘ on November 14th, 2018].  The blend includes formal lectures, example classes, homework assignments, assessed coursework questions and an on-line course, which I delivered as a MOOC a couple of years ago [see ‘Engaging learners on-line‘ on May 25th, 2016].  It is not unusual in a large class, nearly two hundred students this year, that no one asks questions during the lecture; although, at the end of each lecture and example class, a small group of students with questions always forms.  The on-line course has extensive opportunities for asking questions and discussing issues with the instructor and fellow learners.  These opportunities  were used heavily when the course was offered as a MOOC  with 6600 comments posted or 1 every 7.7 minutes!  However, this year the undergraduates have not made any on-line comments and it was a similar situation last year.  Is this a case of pluralistic ignorance?  The term was coined by psychologists Daniel Katz and Floyd Henry Allport in 1931 to describe students who pretend to understand everything explained in class and don’t ask any questions because they believe everyone else in the class has understood everything and they don’t want to damage their reputation with their peers.  Perhaps we have all done it and been very grateful when someone has asked the question that we wanted to ask but did not dare.  Would be it ethical to pretend to be a student and post questions on-line that I know from the MOOC they are likely to want to ask?

Sources:

Patterson EA, Using everyday engineering examples to engage learners on a massive open online course, IJ Mechanical Engineering Education, in press.

Katz D & Allport FH, Students’ attitude, Syracuse, NY: Craftsmann, 1931.

Origgi G, Reputation: what it is and why it matters, Princeton, NJ: Princeton University Press, 2018.

Image: Author speaking at National Tsing Hua University, Taiwan

Blended learning environments

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This is the last in the series of posts on Creating A Learning Environment (CALE).  The series has been based on a workshop given periodically by Pat Campbell [of Campbell-Kibler Associates] and me in the UK and USA, except for the last one on ‘Learning problem-solving skills’ on October 24th, 2018 which was derived on talks I gave to students and staff in Liverpool.  In all of these posts, the focus has been on traditional forms of learning environments; however, almost everything that I have described can be transferred to a virtual learning environment, which is what I have done in the two MOOCs [see ‘Engaging learners on-line’ on May 25th, 2016 and ‘Slowing down time to think (about strain energy)’ on March 8th, 2017].

You can illustrate a much wider range of Everyday Engineering Examples on video than is viable in a lecture theatre.  So, for instance, I used my shower to engage the learners and to introduce a little statistical thermodynamics and explain how we can consider the average behaviour of a myriad of atoms.  However, it is not possible to progress through 5Es [see ‘Engage, Explore, Explain, Elaborate and Evaluate’ on August 1st, 2018] in a single step of a MOOC; so, instead I used a step (or sometimes two steps) of the MOOC to address each ‘E’ and cycled around the 5Es about twice per week.  This approach provides an effective structure for the MOOC which appears to have been a significant factor in achieving higher completion rates than in most MOOCs.

In the MOOC, I extended the Everyday Engineering Example concept into experiments set as homework assignments using kitchen equipment.  For instance, in one lab students were asked to measure the efficiency of their kettle.  In another innovation, we developed Clear Screen Technology to allow me to talk to the audience while solving a worked example.  In the photo below, I am calculating the Gibbs energy in the tank of a compressed air powered car in the final week of the MOOC [where we began to transition to more sophisticated examples].

Last academic year, I blended the MOOC on thermodynamics with my traditional first year module by removing half the lectures, the laboratory classes and worked example classes from the module.  They were replaced by the video shorts, homework labs and Clear Screen Technology worked examples respectively from the MOOC.  The results were positive with an increased attendence at lectures and an improved performance in the examination; although some students did not like and did not engage with the on-line material.

Photographs are stills from the MOOC ‘Energy: Thermodynamics in Everyday Life’.

CALE #10 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA] – although this post is based on recent experience in developing and delivering a MOOC integrated with traditional learning environments.

Student success and self-efficacy

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Success is a multiplicative function of ability and motivation [Chan et al, 1998 & Pinder 1984] and in turn motivation requires positive ‘situation expectations’ and good ‘achievement striving’, which is the extent to which individuals take their work seriously [Norris & Wright, 2003].  Hence, we can motivate engineering students by setting engineering science in a professional context and connecting it to something familiar according to Sheppard et al [2009].

Self-efficacy is a ‘belief in one’s capabilities’ and is closely related to student success [Marra et al, 2009].  There are four sources of self-efficacy that contribute to success: mastery experiences; social persuasion; psychological state; and vicarious experiences [Bandera, 1997].

Mastery experiences include, for example, the positive experience of completing a course or a project.  Vicarious experiences are those gained via observation of someone else’s engagement and their effect on self-efficacy is dependent on similarity of the observer and observed.

The bottom-line is that self-efficacy is powerful motivational construct relating to choices to engage in class activities and to persist in engineering [Hackett et al, 1992].  So, to create a learning environment that motivates all students to acquire knowledge, it is necessary provide opportunities for all sources of self-efficacy to contribute to student success.  This implies providing opportunities for mastery and vicarious experiences in a supportive environment that avoids any negative stereotyping.

Using a variety of everyday engineering examples provides a level of familiarity that lowers anxiety levels and improves the psychological state of students.  Demonstrating everyday examples in class, as part of the Engage step in the 5Es [see ‘Engage, Explore, Explain, Elaborate and Evaluate’ on August 1st, 2018], allows students to have a vicarious experience as does Elaborating examples for them.  While allowing students to Evaluate their own learning provides the opportunity for mastery experiences.  These factors are probably one reason why using Everyday Engineering Examples embedded in 5E lesson plans leads to a higher level of student engagement and learning.

References:

Bandura A, Self-efficacy: the exercise of control, Freeman & Co, New York, 1997.

Chan D, Schmitt N, Sacco JM; DeShon RP. Understanding pretest and posttest reactions to cognitive ability and personality tests, J. Applied Psychology, 83(3): 471-485, 1998

Hackett G, Betz NE, Casas JM, Rocha-Singa IA, Gender ethinicity and social cognitive factors predicting the academic achievement of students in engineering, J. Counselling Psychology, 39(4):527-538, 1992.

Marra RM, Rodgers KA, Shen D, and Bogue B, Women engineering students and self-efficacy: a multi-year, multi-institution study of women engineering student self-efficacy, J. Engineering Education, 99(1):27-38, 2009.

Norris SA, Wright D. Moderating effects of achievement striving and situational optimism on the relationship between ability and performance outcomes of college students, Research in Higher Education, 44(3):327-346, 2003.

Pinder CC, Work motivation, Scott, Foresman Publishing, Glenview, IL, 1984.

Sheppard S, Macatangay K, Colby A, Sullivan WM, Educating engineers: designing for the future of the field, Jossey-Bass, San Francisco, CA, 2009.

 

CALE #8 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]

Engineering idiom

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Many of us, either as students or as instructors, will have experienced the phenomenon that students are more likely to give a correct answer when the context is familiar [Linn & Hyde, 1989; Chapman et al 1991].  Conversely, a lack of familiarity may induce students to panic about the context and fail to listen in a lecture [Rosser, 2004] or to appreciate the point of a question in an examination.  Your dictionary probably gives two meanings for context: ‘surrounding conditions’ and ‘a construction of speech’.  You would think that the importance of teaching by reference to the surrounding condition is so obvious as to require no comment; except professors forget that conditions experienced by students are different to their own, both now and when they were students [Nathan, 2005 & ‘Creating an evolving learning environment’ on February 21st, 2018].  To get an appreciation of how different consult the ‘Mindset List‘ produced each year by Beloit College; for example as far as the class of 2020 are concerned robots have always been surgical partners in the operating room [#55 on the 2020 Mindset List].

What about the construction of speech?  I think that there is an engineering idiom because engineering education has its own ‘language’ of models and analogies.  Engineering science is usually taught in the context of idealised applications, such as colliding spheres, springs and dashpots, and shafts.  It would be wrong to say that they have no relevance to the subject; but, the relevance is often only apparent to those well-versed in the subject; and, by definition, students are not.  The result is a loss of perceived usefulness of learning which adversely influences student motivation [Wigfield & Eccles, 2000] – they are more likely to switch off, so keep the language simple.

References:

Chipman S, Marshall S, Scott P. Content effects on word problem performance: A possible source of test bias? American Educational Research Journal, 28(4), 897-915, 1991.

Linn M, Hyde J, Gender, mathematics, and science, Educational Researcher, 18(8), 17-19, 22-27, 1989.

Nathan R, My freshman year: what a professor learned by becoming a student, Cornell University Press, Ithaca, New York, 2005.

Rosser SV, Gender issues in teaching science, in S. Rose. and B. Brown (eds.), Report on the 2003 Workshop on Gender Issues in the Sciences, pp. 28-37, 2004.

Wigfield A, Eccles JS, Expectancy-value theory of motivation, Contemporary Educational Psychology, 25(1): 68-81, 2000.

 

CALE #7 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]