The TPS Report

My general research interests in the field of engineering education lie somewhere near the interface of transfer of learning (primary area), collaborative learning (secondary area I), and student motivation and engagement (secondary area II) in the contextual setting of cross-disciplinary, student-based undergraduate engineering design teams.  With a general interest in the primary area of transfer of learning (also known as transfer of practice, knowledge transfer, and transfer of skill), promoting mindful learning over mindlessness is not sufficient but necessary.

First, interest in transfer of learning as a phenomenon relevant to the co-construction of knowledge is motivated by the significant role it plays in formal education.  To be clear, “[t]ransfer of learning is universally accepted as the ultimate aim of teaching” (McKeough, Lupart, & Marini, 1995, p. vii) and “widely considered to be a fundamental goal of education” (Marini & Genereux, 1995, p. 1).  Ellis (1965), Haslerud (1972), Cormier and Hagman (1987), Detterman and Sternberg (1993), the National Academy of Engineering (2000), and Mestre (2005) concur.  Explained, Tuomi-Grohn, Engestrom, and Young (2003) argue that “[s]chools are not able to teach students everything they will need to know for the rest of their lives; they must equip students with the ability to transfer – to use what they have learned to solve new problems successfully or to learn quickly in new situations” (p. 1; emphasis added).

Now, consider the following from (Langer, 2000): “When we are mindless, our behavior is rule and routine governed; when we are mindful, rules and routines may guide our behavior rather than predetermine it” (p. 220; emphasis added).  In other words, the promotion of mindful learning will prepare students for future learning, teaching them to adaptively transfer generalized rules and routines to novel contexts.

Next, while co-constructing knowledge, students interact with three different features of content – incidental features, surface features, and deep features (Schwartz & Nasir, 2003).  First, incidental features are characteristics of the context surrounding a concept, not the concept itself.  They can be thought of as distractors.  Second, surface features are characteristics of a concept that might promote deep understanding of the concept but are in no way necessary for understanding it.  Third, deep features are characteristics of a concept so fundamental to the definition of the concept that they are absolutely necessary to understand it.  Overall, successful transfer of learning necessitates that the deep features of a concept be brought to the foreground and, at the same time, the surface and incidental features be forced into the background.

Again, consider the following from (Langer, 2000): “Mindfulness is a flexible state of mind in which we are actively engaged in the present, noticing new things and sensitive to context.  When we are in a state of mindlessness, we act like automatons who have been programmed to act according to the sense our behavior made in the past, rather than the present.” (p. 220; emphasis added).  In other words, mindfulness equips students, via a prior context, with the functional structures of phenomena necessary to process said phenomena in a new context.

On balance, active learning instructional methods, including, but not limited to, problem-based learning units, appear to stand in contrast to the banking concept of education.  Explained, Freire (1993) lists attitudes and practices characteristic of banking education as follows:

the teacher teaches and the students are taught[,]

the teacher knows everything and the students know nothing[,]

the teacher thinks and the students are thought about[,]

the teacher talks and the students listen–meekly[,]

the teacher disciplines and the students are disciplined[,]

the teacher chooses and enforces his choice, and the students comply[,]

the teacher acts and the students have the illusion of acting through the action of the teacher[,]

the teacher chooses the program content, and the students (who were not consulted) adapt to it[,]

the teacher confuses the authority of knowledge with his or her own professional authority, which she and he sets in opposition to the freedom of the students[, and ]

the teacher is the Subject of the learning process, while the pupils are mere objects. (p. 73)

However, human beings do not learn by the brain absorbing information like a sponge absorbs water.  Rather, the process of learning is iterative in nature, a series of lived experiences wherein conceptual understanding is constructed from one experience to another.  To best illustrate the difference, let us reconsider the case of the sponge.  The sponge is not an autonomous being.  Accordingly, it does not choose the liquid in which it will be submerged and then absorb.  Instead, the sponge is placed in a liquid and, consequently, forced to absorb it.  In terms of learning theory, the case of the sponge is most analogous to banking education.

On the other hand, Jonassen (2014) succinctly characterized problem-based learning as problem-focused, student-centered, self-directed, and self-reflective.  In general, problem-based learning requires students to engage in their own learning processes by working in small groups, with instructors embedded as tutors, on problem scenarios presented in the form of case study materials.  More specifically, problem-based learning includes, on balance, the following five features:

1. “Complex, real world situations that have no one ‘right’ answer are the organizing focus for learning.
2. Students work in teams to confront the problem, to identify learning gaps, and to develop viable solutions.
3. Students gain new information [through] self-directed learning.
4. Staff act as facilitators.
5. Problems lead to the development of clinical problem-solving capabilities” (Savin-Baden & Howell Major, 2004, p. 4; emphasis added).

Taking together all of the above added emphasis, problem-based learning should treat students as active participants in their own learning by encouraging them to connect information they receive from an interaction with their environment and, in turn, reflecting on it.  In doing so, students build meaning or construct understanding of a certain concept.  They then turn to those concepts when they encounter new experiences (Crain, 2005).

Furthermore, “the mental model learned is a ‘construction’ by the learner.  As a result each person has a slightly different model that is a combination of all of his or her past experiences and his interpretations of the current situation (Newstetter and Svinicki, 2014, p. 35; emphasis added).  Johri and Olds (2011) explain that “knowledge is socially reproduced and learning occurs through participation in meaningful activities that are part of a community of practice[,] … participation which is mutually constituted through and reflects our thinking and literacy skills” (p. 160; emphasis added).  Problem-based learning, as well as guided inquiry-based learing, best provide the authentic problem solving and learning environments indicative of a socially constructivist instructional method, not the banking concept of education (Newstetter & Svinicki, 2014).

As a graduate student in the educational psychology and research methodology master’s program at Purdue University-West Lafayette, I experienced the only distance learning course in which I have enrolled to date – EDPS 55700: Introduction to Quantitative Data Analysis Methods in Education II.  To clarify, this is a course that is equivalent to the second in a two-course sequence of graduate-level statistics (i.e., applied regression analysis).

Although deliverables for the course were scheduled to be submitted at predetermined dates of varying time intervals, all materials related to the course were released when classes began.  Those materials included, but were not limited to, a course syllabus, a readings list, lecture notes, the deliverables, assessment templates, and assessment rubrics.

Fortunately, I transferred into the course significant prior knowledge of statistics from my experience as an undergraduate student in the College of Engineering and was thereby able to treat it as a self-guided online course.  Therefore, the course was an overwhelming positive experience for me because I was able to tailor the course schedule to my overall schedule.  However, I cannot speak to the quality of the course for other students, specifically those with limited to no prior statistical knowledge.  Consequently, I would argue the fact that the course could be treated in such a hyper-asynchronous manner speaks, in and of itself, as a valid indictment of the general course offering.

On a related note, I found it interesting that, of the 17 instructors expert in online and distance learning included in the 2017 Inside Higher Ed article “Take My Advice” by Jean Dimeo, zero reported teaching statistics-related content.  To be clear, I am not implying a causal relationship but rather simply reporting what is, in my opinion, an interesting nugget.

I would not be inclined to even apply for an academic position with a significant online teaching load because I rely (or, arguably, excessively rely) on immediate facial feedback from students while teaching.  However, if I were to design courses for a digital environment, then I would emphasize both the seventh (i.e., Scaffold Learning Activities) and the eighth (i.e., Provide Examples) essential principles and practices advised by Flower Darby in the 2019 Chronicle of Higher Education article “How to Be a Better Online Teacher”.  First, scaffolding learning activities would leverage student zones of proximal development.

Explained, the zone of proximal development, as developed by social-historical theorist L. S. Vygotsky, is “the gap between what a learner can accomplish on his or her own and what he or she can attain in collaboration with someone more expert” (Newstetter & Svinicki, 2014, p. 40).

In other words, a student does not learn best when presented with a task he or she either [a] can already complete on his or her own, without assistance, or [b] cannot complete, even with assistance.  Instead, a student learns best when presented with a task that he or she can nearly complete on his or her own, thereby requiring attenuated guidance from the instructor or collaboration with other students.

Vygotsky called the process of offering this attenuated guidance scaffolding because the guidance is “like a temporary scaffold that comes down when construction is finished.  For example, a parent might initially help a child pedal and steer a tricycle, but then step aside as the child seems able to ride it on her own” (Crain, 2005, p. 241).  The presence of such scaffolding should also prevent the student from growing bored, as well as confused to the extent that he or she simply gives up on the task.

Moreover, the act of providing examples can itself best scaffold learning activities as contrasting cases.  From the Adaptive and Advanced Learning and Behavior Lab in the Graduate School of Education at Stanford University, “[c]ontrasting cases are collections of problems or examples that help students understand the quantitative structure of empirical phenomena.  They are designed to present optimal variation for learning functional structures that capture how distinct quantifiable elements combine to make new properties (e.g. density is a ratio of mass over volume).”

“Digital Pedagogy in the Humanities: Concepts, Models, and Experiments is a curated collection of reusable and remixable pedagogical artifacts for humanities scholars in development by the Modern Language Association.”  The following keywords were reviewed:

1. Assessment – “While there is no universal agreement or measure on understanding what students learn, Thomas Angelo defines assessment as an ‘ongoing process aimed at understanding and improving student learning.  It involves making our expectations explicit and public; setting appropriate criteria and high standards for learning quality; systematically gathering, analyzing, and interpreting evidence to determine how well performance matches those expectations and standards; and using the resulting information to document, explain, and improve performance.’  All assessment practices involves the collection and analysis of student work against a set of standards.”
2. Collaboration – “Collaboration by difference is especially valuable in classroom environments.  Julia Gergits and James Schramer note that ‘fruitful collaboration often starts with the recognition that difference is essential if a group wishes to generate truly original ideas.’  According to Andrea Lunsford and Lisa Ede, assignments that are labor-intensive, require multiple areas of expertise, or involve synthesis of divergent perspectives are best suited to collaboration.  These can range from low-stakes, informal, in-class activities to larger long-term projects … Evaluating collaborative work can be a significant challenge when working within dominant, individualistic models of knowledge production.  Collaboration can also make people feel uncomfortable, as it requires relinquishing control over a project.”
3. Visualization – “Given the proliferation of visualizations in online media, especially for exploring big data, it has become important to teach students to treat them critically.  One way to do that is to have students fiddle with different types of visualization and reflect on the graphical features and how they might reflect evidence or not.  Having students then create their own visualizations is an effective way of encouraging them to leverage their humanities training in the interpretation of a variety of contemporary issues, from the environment to various social inequalities.  Visualizations are an eminently shareable form of communication and students can be empowered to communicate through visualizations, sharing results via social media.”

With respect to collaboration, the unfortunate reality is that there are challenges to obtaining collaboration characterized by explanation and mutual negotiation, given students’ limited previous experience with group work and lack of skills for effective collaboration (Barron, 2003; Roschelle & Teasley, 1995; Webb & Mastergeorge, 2003).  First, individual activities pervade American schools, fostering an environment of isolation characterized by competition, not cooperation, among students seeking the “right” answer and a “good” grade (Blumenfeld, Marx, Soloway, & Krajcik, 1996).  Second, on the occasion in which students are provided with an opportunity to work in groups, collective activities tend to promote lower-order cognitive skills such as rehearsal and memorization (Slavin, 1996).

However, computer-supported collaborative learning (CSCL) can function as a vehicle for overcoming the challenges to obtaining rich collaborative learning experiences by fostering collective efficacy (Wang, Hsu, Lin, & Hwang, 2014; Wang & Hwang, 2012).  Bandura (1997) theorized the construct of collective efficacy, which combines each group member’s (a) individual efficacy and (b) evaluation of the efficacy of the group as a whole.  Bandura (1997) elaborated that “[b]elief of collective efficacy affects the sense of mission and purpose of a system, the strength of common commitment to what it seeks to achieve, how well its members work together to produce results, and the group’s resiliency in the face of difficulties” (p. 469).  Collective efficacy warrants exploration given that, “not only individual processes, but also social processes and relationships influence students’ motivation to learn and to achieve[,]” thereby highlighting the important role cooperation between group members plays in the further development of conceptual understanding (Loyens, Rikers, & Schmidt, 2007, p. 181).

Loyens, Rikers, and Schmidt (2007) explained that, “not only individual processes, but also social processes and relationships influence students’ motivation to learn and to achieve[,]” thereby highlighting the important role cooperation between group members plays in the further development of conceptual understanding (p. 181).  Unfortunately, the reality is that there are challenges to obtaining collaboration characterized by explanation and mutual negotiation, given students’ limited previous experience with group work and lack of skills for effective collaboration (Barron, 2003; Roschelle & Teasley, 1995; Webb & Mastergeorge, 2003).  First, individual activities pervade American schools, fostering an environment of isolation characterized by competition, not cooperation, among students seeking the “right” answer and a “good” grade (Blumenfeld, Marx, Soloway, & Krajcik, 1996).  Second, on the occasion in which students are provided with an opportunity to work in groups, collective activities tend to promote lower-order cognitive skills such as rehearsal and memorization (Slavin, 1996).

However, problem-based learning can function as a vehicle for overcoming the challenges to obtaining rich collaborative learning experiences.  In general, the ambiguity associated with problem-based learning units requires students to both construct applicable knowledge and develop particular skills.  Blumenfeld, Soloway, Marx, Krajcik, Guzdial, and Palincsar (1991) explained that “students need to … generate plans, systematically make and test predictions, interpret evidence in light of those predictions, and determine solutions” in order to successfully navigate the complex nature of problem-based learning units (p. 378).  If students are indeed successful in completing such tasks, Meyer, Turner, and Spencer (1997) concluded that said students are more favorable to “trying out ideas, learning from mistakes, and persisting[,]” all of which are characteristic of more efficacious students (p. 517).

More specifically, Godfrey (2014) explained that “the culture of engineering education is not only that of an academic discipline with a distinctive knowledge domain influenced by the institutional structures and traditions of higher education … but also incorporates cultural influences from the engineering profession” (p. 439).  Therefore, the adoption of problem-based learning is “an excellent example of the benefits of looking at what people need to do once they graduate and then crafting educational experiences that best prepare them for these competencies” (National Academy of Engineering, 2000, p. 77; emphasis added).  Jonassen (2014) further explained that, “[b]ecause engineering students learn to solve problems that are unlikely to transfer to workplace problem solving, engineering educators must adopt new pedagogies if they are committed to enabling their graduates to become effective engineers” (p. 112).  Problem-based learning is such a pedagogy, with employer evaluations of undergraduate engineering programs incorporating problem-based learning rating superior on project and people management, innovative and creative skills, and engineering and technical quality (Litzinger, Lattuca, Hadgraft, & Newstetter, 2011).

The optimal execution of active learning instructional methods, in general, and problem-based learning units, more specifically, that I have experienced was through my role as a graduate research assistant in a cross-disciplinary research collaboration between the College of Education and the College of Veterinary Medicine at Purdue University-West Lafayette.  While a graduate student in the Department of Educational Studies, I designed and analyzed a research program for investigating the development of both individual efficacy beliefs and collective efficacy beliefs in a problem-based learning environment.  The College of Veterinary Medicine was an ideal candidate for exploring problem-based learning because, historically, problem-based learning has been primarily associated with medical, nursing, and veterinary curricula (Prince & Felder, 2006).

The College of Veterinary Medicine is accredited by the American Veterinary Medical Association (AVMA) and “has gained an international reputation for its curriculum, which integrates a traditional lecture-style format for teaching with problem-based learning” (Bayer Animal Health, 2007).  Moreover, the three faculty members who were responsible for developing this unique hybrid curriculum and continue to teach the problem-based learning series of courses have all been awarded the Association of American Veterinary Medical Colleges (AAVMC) Distinguished Veterinary Teacher Award, “the most prestigious teaching award in veterinary medicine” (AAVMC, 2016).

Members of the Doctor of Veterinary Medicine Class of 2020 (then first-year students) served as research subjects through their enrollment in the course Veterinary Medicine (VM) 82000 – Applications and Integrations I.  VM82000 is the first in the series of four problem-based learning courses referred to above that are centered on authentic cases requiring students to engage in the following: identification of learning issues, resolution of identified gaps in their knowledge bases, integration of information across courses, and development of problem-solving skills.

Before the semester begins, the class is divided into twelve groups of seven students, with each group assigned a faculty tutor who immerses himself or herself with the students.  While the students in each group are static to maintain balanced subject matter expertise, the faculty tutor assigned to each group is rotated among all of the groups at three different points during the semester.

Closed-loop problem-based materials are studied with the emphasis on applying content knowledge that is being concurrently learned in required courses, as well as fostering professional skills (Barrows, 1986).  The loop is closed after each problem-based learning unit with a consolidation lecture given by the instructor-of-record, who will lead a discussion of the threshold concepts.  The consolidation lecture begins with each group briefly “shar[ing] their problem representations and solution methods and strategies … to compare and contrast the effectiveness of those representations and solution methods.”  The consolidation lecture ends with the instructor-of-record “shar[ing] the canonical ways of representing and solving the problems with the class” (Kapur, 2010, p. 527).

For nearly the past two decades, four letters have been omnipresent in discussions related to the future of education in the United States of America – S-T-E-M.  The acronym STEM represents the academic disciplines of science, technology, engineering, and mathematics.  The push since 2001 for the addition of STEM to the American educational lexicon has simply been rooted in pragmatism. To illustrate, consider the following statement from the White House Office of Science and Technology Policy (OSTP): “[M]ore and more, well-paying jobs–especially those in the science, technology, engineering[,] and mathematics (STEM) fields–require education or training after high school.  STEM fields are some of the fastest growing sectors of the American economy and they are important to the health and longevity of our Nation’s people, economy, and environment” (Seraphin & Carnival, 2013, para. 2).

Unfortunately, diversity in science, technology, engineering, and mathematics has been anything but universal.  Apart from Asians, minorities have been underrepresented in both the STEM disciplines at colleges and universities and the STEM fields in the workforce.  These minorities include women (50.2% of total population), blacks (12.7% of total population), Hispanics (17.0% of total population), and American Indians (2.5% of total population) (National Science Foundation, 2017).  Consider the following examples:

• The share of bachelor’s degrees in engineering earned by women was less than 20% each year from 1991 to 2014, and trending down.
• The share of bachelor’s degrees in both science and engineering earned by blacks, Hispanics, and American Indians was less than 20% each year from 1991 to 2014, and holding flat.

A logical, but simple, purpose for increasing diversity in science, technology, engineering, and mathematics is, thus, to supply the demand for as many as three million STEM workers by harnessing the untapped potential of women, blacks, Hispanics, and American Indians in STEM disciplines at colleges and universities.  However, the importance of diversity in STEM is much more complex than just a staffing issue, reducing the importance of underrepresented minorities (URM) to just available warm bodies.  Rather, the importance of diversity in STEM is a quality issue.

In the 2002 book Diversity in Engineering: Managing the Workforce of the Future, the National Academy of Engineering (NAE) Committee on Diversity in the Engineering Workforce argued that diversity in engineering, while both a fairness issue and an equity issue (as well as a staffing issue,) is much more of an issue related to creativity.  Explained, the NAE claimed that “creativity is not something that just happens.  It is the result of making unexpected connections between things we already know” (p. 8).  In other words, the creativity of an individual is inextricably linked to both the personal experiences and the prior knowledge of that individual and that individual alone.  Dr. William A. Wulf, President of the NAE from 1996 to 2007, concluded the following:

The range of possible solutions to an engineering problem will be smaller from a nondiverse design team, and the elegant solution to a human problem may not be among them.  That limitation can have substantial, but hidden, economic costs, opportunity costs, costs that must be measured in terms of designs not considered. (2002, p. 12; emphasis added)

Dr. Cornelius F. Lane, Director of the White House OSTP from 1998 to 2001, put it simply as follows: “Homogeneity makes us stale – we need diverse backgrounds and perspectives to keep our lead in this age of innovation” (1999, p. 19).