Category Archives: Creating A Learning Environment (CALE)

Blended learning environments

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.

Learning problem-solving skills

Inukshuk: meaning ‘in the likeness of a human’ in the Inuit language. A traditional symbol meaning ‘someone was here’ or ‘you are on the right path’.

One definition of engineering given in the Oxford English Dictionary is ‘the action of working artfully to bring something about’.  This action usually requires creative problem-solving which is a common skill possessed by all engineers regardless of their field of specialisation.  In many universities, students acquire this skill though solving example problems set by their instructors and supported by example classes and, or tutorials.

In my lectures, I solve example problems in class using a pen and paper combined with a visualiser and then give the students a set of problems to solve themselves.  The answers but not the solutions are provided; so that students know when they have arrived at the correct answer but not how to get there.  Students find this difficult and complain because I am putting the emphasis on their learning of problem-solving skills which requires considerable effort by them.  There are no short-cuts – it’s a process of deep-learning [see ‘Deep long-term learning’ on April 18th, 2018].

Research shows that students tend to jump into algebraic manipulation of equations whereas experts experiment to find the best approach to solving a problem.  The transition from student to skilled problem-solver requires students to become comfortable with the slow and uncertain process of creating representations of the problem and exploring the possible approaches to the solution [Martin & Schwartz, 2014].  And, it takes extensive practice to develop these problem-solving skills [Martin & Schwartz, 2009].  For instance, it is challenging to persuade students to sketch a representation of the problem that they are trying to solve [see ‘Meta-representational competence’ on May 13th, 2015].  Working in small groups with a tutor or a peer-mentor is an effective way of supporting students in acquiring these skills.  However, it is important to ensure that the students are engaged in the problem-solving so that the tutor acts as consultant or a guide who is not directly involved in solving the problem but can give students confidence that they are on the right path.

[Footnote: a visualiser is the modern equivalent of an OverHead Projector (OHP) which instead of projecting optically uses a digital camera and projector.  It’s probably deserves to be on the Mindset List since it is one of those differences between a professor’s experience as a student and our students’ experience [see ‘Engineering idiom’ on September 12th, 2018]].

References:

Martin L & Schwartz DL, A pragmatic perspective on visual representation and creative thinking, Visual Studies, 29(1):80-93, 2014.

Martin L & Schwartz DL, Prospective adaptation in the use of external representations, Cognition and Instruction, 27(4):370-400, 2009.

 

CALE #9 [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 an introduction to tutorials given to new students and staff at the University of Liverpool in 2015 & 2016.

Photo: ILANAAQ_Whistler by NordicLondon (CC BY-NC 2.0) https://www.flickr.com/photos/25408600@N00/189300958/

Student success and self-efficacy

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

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]