This Nutshell begins a series that focuses on increasing students' learning and their enthusiasm for learning. The series taps details from past Nutshells and our institutional on-line subscription to National Teaching and Learning Forum. These are available through on-line archives at http://www.isu.edu/ctl/nutshells/index.html and http://www.ntlf.com/restricted/ respectively. The latter site is available only from computers on the ISU campuses.

 

President Vailas' July 5 Convocation message conveyed that student retention is everyone's challenge at ISU. Normally, I cease writing Nutshells in the summer when most faculty are away, but the retention initiative is an important one that we can get behind quickly, and this makes it useful to kick-start the year with some summer issues. Retention increases when students are both successful and enthused learners. In an optimal setting for learning, students: (1) are aware of the differences in approach needed to master surface learning and deep learning; (2) have clear messages about what constitutes high expectations; (3) feel supported in their efforts to meet these expectations by an active learning community with a signature identity, and (4) can self-assess and derive satisfaction from the quality of their learning. We will deal mainly with the first of the four in this Nutshell. In the final issue of the series, we'll deal with assessment tools that are both useful to promote good learning design and show that specific learning occurred.

 

Achieving the four components involves being attentive to students' cognitive, affective, and psychomotor domains. In January 2003, Bob Leamnson delivered the first February University-wide faculty development workshop to over 110 ISU faculty. Approximately 160 ISU faculty now own Leamnson's book, Thinking About Teaching and Learning, which addresses learning at the neurological level as the building and stabilization of synaptic connections (NNv8n8&9; NNv11n1). As instructors, we find it obvious to strive to develop cognitive growth related to the content of our disciplines. Less obvious is the fact that the neural network of the cognitive domain we seek to develop is inextricably connected with the affective and kinesthetic (psychomotor) domains. Student success that leads to retention involves understanding of how to employ all three—the more of the brain that we design our learning activities to employ, the more neural connections our students are able to build.

 

Surface learning involves largely what students know. Knowing rests largely in the lower two levels of recall and comprehension of Bloom's taxonomy of the cognitive domain (NNv9n1) and in many simple computational challenges of Bloom's third level (application). Placing recall and comprehension in the lower cognitive levels does not translate into these being easy tasks, and an inability to learn large amounts of factual information quickly can be discouraging and cause students to give up. A way to assist with the difficulty is to first design good learning activities as models and second to teach students how to design their own in order to manage these learning tasks. Lecturing facts to students and simply telling them to go and memorize is perhaps the least effective of all methods to promote desired learning. In-class games and drills (see visible quiz in NNv12n2 at http://www.isu.edu/ctl/nutshells/nutshell12-2.html) puzzles (crosswords are good), and content-rich games done in pairs and groups with short discussions at the start of class are much better. The challenges posed by the drills should represent in content and difficulty the challenges on graded tests that we will hold students accountable to know. The best learning occurs after students master design of their own memory aids and learning enhancement exercises. One may catalyze this after the class has experienced several instructor-designed lessons for learning as models. Students are then assigned to design and provide a learning experience for a block of content for the rest of their small group. The act helps to convey how much work that it actually takes to master a block of low-level knowledge and does so by providing a support group (nurtures affective domain) through which to encourage, discuss, and develop this very necessary awareness about learning. In contrast to learning in isolation and silence through rote memorization, group discussion and visualization draw in involvement of the psychomotor domain, thus building and stabilizing more synaptic connections at a faster pace. The second February development workshop in 2004 featured Dr. Barbara Millis, who provided training to over 120 ISU faculty. Other ISU attendants at the Boot Camp for Profs® program bring the total ISU faculty who have achieved training and have Millis' and Cottell's book, Cooperative Learning for Higher Education Faculty, to about 160 . Consult this book for help in designing your own learning exercises with groups.

 

In contrast to knowing, deep learning focuses largely on expanding what students can do. Students who succeed at deep learning must not merely be exposed to the higher Bloom stages of synthesis and evaluation, but they must eventually understand what it means to do synthesis and evaluation well (see NNv10n1&n2). Such sophistication in achievement of high level thinking skills requires (a) awareness of employing a framework of reasoning; (b) a good use of evidence, and (c) self- reflection for metacognitive awareness. The fourth University-wide faculty development workshop in 2006 with Susan Wolcott (see http://www.idea.ksu.edu/papers/Idea_Paper_37.pdf) focused on the differences in thinking between students who value only surface learning and students able to perceive deep learning as the outcome of a higher quality education. About 160 ISU faculty have King and Kitchener's Developing Reflective Judgment book, which offers detailed research about the characteristics of student achievement displayed at different levels of thinking.

 

Why should students' lack of awareness about surface learning and deep learning be related to retention? A part of the answer is that most students are unable to distinguish becoming credentialed with a degree to becoming educated through acquiring higher level thinking abilities. Such students see a college degree as a ticket to getting a job but don't think beyond job acquisition to acquiring skills needed for either keeping that job or for career advancement. The view of education-as-ticket leads to perceiving any content not immediately applicable to their chosen specialized majors as a delaying impediment. Curricular requirements then become viewed as simply obstacles to overcome through seat time spent in surface learning of more facts. On the other hand, if a student perceives the nature of deep learning, she/he understands the content as opportunity to master varied frameworks of reasoning and to deal effectively with divergent, open-ended problems that typify real career challenges in making sound, informed decisions. These skills, rather than surface learning, are what provide the ability for career advancement or to transition rapidly into new areas of opportunity.

 

Generating and assessing deep learning involves work that is initially neither easy for students nor professors. Learning for short-answer tests that define achievement based largely on knowing as manifested in test-taking skills under timed conditions is no longer sufficient. Instead, deep learning requires students to develop other neural networks that can deal in sophisticated ways with open-ended challenges through projects and written reports that involve students' generating products through discussion, reflection, and revision. These serve as much to promote learning and to mentor students to high-level thinking as to produce grades. Students will initially resist changes toward higher level thinking (see NNv8n3) unless/until they can grasp the essence and purpose of it. If institutions do not support both professors and students in this difficult transition, the institutional signature dissolves into what George Kuh (Change Magazine, 2003, v. 35, n. 2) terms "the disengagement compact: ‘I'll leave you alone if you leave me alone.' That is, I won't make you work too hard (read a lot, write a lot) so that I won't have to grade as many papers or explain why you are not performing well."

 

The neural development changes that allow the shift from shallow to true deep learning require longer than a sixteen-week semester and cannot be achieved through a single course. However, a planned curriculum that develops these abilities over several semesters can achieve desired results (Pavelich and Moore, 1996, Journal of Engineering Education, October, pp. 287-292). Without such curricula, students' reasoning abilities change little between high school and college graduations. Students in a school permeated by Kuh's "disengagement compact" can be totally satisfied and oblivious to the severe disservice being done through such a compact.

 

Students should receive an introduction to the differences between shallow and deep learning in their orientations and first year seminar experiences. This introduction needs to be reinforced repeatedly in subsequent courses until familiarity becomes part of the institutional culture.

 

 

The Center for Teaching and Learning will begin a special series of Friday noon - 1:00 workshops on the theme "Teaching for Student Success" in
Museum Building 432.
Watch for further announcements.

Click here to see New Faculty Orientation Schedule August 15 & 16, 2006!

If you have new faculty in your units, please avoid causing conflicts for them by scheduling meetings, etc. on these dates.