Peerwise

Peerwise is a collaborative web-based system that allows students to create and evaluate a test bank of multiple choice questions. The pedagogical motivation behind this system is that students can learn better if they go through a process of self-reflection (meta-cognition), identify / synthesize / evaluate (higher levels of Bloom taxonomy), and articulate the subtleties in a concise format. Denny et al. (2010) show that students who were most active using the system improved their rank in the class relative to their peers who were less active. This is measured by using the students' final course grade from the previous course as a baseline for their initial class rank, and comparing with their final course grade in the course that involves the use of Peerwise.

Reference:

Denny, P., Hanks, B., Simon, B. (2010). PeerWise: Replication Study of a Student-Collaborative Self-Testing Web Service in a U.S. Setting. SIGCSE 2010, March 10-13.

Learner's Styles, Aptitudes, Personalities .. do they make a difference?

Learning styles refer to the different ways different people learn information (e.g. visual / audio learners). Learning aptitudes refer to how different people learn in different learning environment structure (e.g. how students learn in highly structured or less structured learning environments). Learner personalities refer to the learner's belief whether his or her successes or failures are a consequence of internal or external factors (e.g. whether students believe their success and failures are a consequence of internal or external factors). Pashler et al. (2009) report that there are inconsistent and insufficient evidences that learning will be effective if instructions are provided in the mode that match learner's styles / attributes / personalities. This does not mean that learners do not have preferences, but in the particular type of evidence that Pashler et al. are looking for, that according to them would be "credible validation of learning-styles-based instruction", such evidence is missing.

The lack of evidence also does not mean that instructors should just stick to one mode of teaching. Students benefit from different representations of information, whether it be verbal, visual, analytical, lecture-based, inductive / deductive reasoning, etc., and that students should not pigeon-holed themselves in learning from any one or two particular styles.

Reference:

Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2009). Learning Styles, Concepts and Evidence. Psychological Science in The Public Interest. 9(3), pp 105- 119.

Student Self-Explanation

Student self-explanation of material they just read has been shown to be effective in producing robust learning gains in a number of disciplines. However, past research results have not been clear whether performance gain is due to student simply paying attention to explanation generated by the instructors, or explanation generated by the students themselves. One research has shown that explanation is more effective when the students generate it rather than simply paying attention to instructor generated explanations (Brown and Kane, 1988), while in another case, the reverse is true (Lovett, 1992). Most recently, Hausmann and Vanlehn (2007) show that generating self-explanation while students attempted solving problems and studying examples is more effective in normal as well as robust learning (which means knowledge is retained over a significant period of time and demonstrated in far transfer of problem solving) than students who comprehended and paraphrased explanations generated by the instructors.

Self-explanation, coupled with learning by examples, can be very effective in student learning. Learning by examples has a lower cognitive load than learning by doing or solving problems, based on cognitive load theory. Thus comparing students who learn by doing a number of questions with those who learn by working through a number of examples, the cognitive load in the latter is much lower, and this affords the students the capacity to come up with general solution principles through self-explanation to improve their effectiveness in learning.

A related theme is that students who self-monitor their learning and comprehension in addition to self-explain the material they learned are better problem solvers than those who don't. By self-monitoring, this means that the students keep track of what they know and what they don't know, what are the parameters and data provided by the problems they are trying to solve, what needs to be solved, how the problems relate to the examples they have worked through having specific goals such as looking for solution methods rather than equations, formulas, similar contexts, etc.

References:

Brown, A.L. and Kane, M.J. (1988). Preschool Children Can Learn to Transfer: Learning to Learn and Learning from example. Cognitive Psychology. 20(4), pp 493 - 523.

Hausmann, R.G.M. and Vanlehn, K. (2007). Explaining Self-Explaining: A Contrast Between Content and Generation. In R. Luckin, K.R. Koedinger, and J. Greer (Eds). Proceedings of Artificial Intelligence in Education (2007). Amsterdam, The Netherlands: IOS Press.

Lovett, M.C. (1992). Learning by Problem Solving versus by Examples: The Benefits of Generating and Receiving Information. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Erlbaum, pp 956 - 961.

Optimized University

Carl Wieman put together a "think piece" on a new model for post-secondary education, which he called the Optimized University. Here are some of the highlights:

• The Optimized University will focus on the desired student education outcomes rather than number of courses / credits students need to graduate with. There will be a switch in focus from processes to outcomes.
• The instructor's role will primarily be an educational designer who continually assesses student's development with the assistance of technology and provides targeted feedback and challenges to the students to optimize their learning rather than simply a one-way transference of knowledge to students.
• Clearly delineated educational goals will be created by relevant faculty in consultation with other stakeholders such as industry, educational systems, and government.
• IT will be used to accurately diagnose student preparation, conceptual knowledge, beliefs, and epistemologies. IT will also be used for new teaching methods (interactive simulations, intelligent tutors, sophisticated diagnostic capabilities, clickers), improved class organization and management systems, archiving systems for educational materials and data, deployment of new modes of presenting material and enhanced communication by linking students with each other and faulty.
• The Optimized University will have sophisticated pedagogical content knowledge - knowledge on how the content and skills are best learned, common student difficulties, approaches most effective in helping students overcome those difficulties, and how to motivate students to master the subject.
• Validated assessments of desired deep understanding of material rather than a simple memorization of facts and problem solving recipes will be in place.
• Technology will be used to make classes more intellectually engaging and educationally effective. Research has shown that there have been demonstrations of classes of 200 or more achieving very good learning gains using clickers and peer instruction in the lectures, computer graded homework systems, student-student collaboration (on / off line), extensive course webpages, and survey systems.
• Carefully constructed diagnostic exams will be used to assess student preparedness and to reduce large hidden cost in instructor's time to provide the unprepared students with extra assistance and in dealing with the repercussions of failing students.
• Student support will range from peer support and intelligent tutoring system, to trained undergraduate and graduate TA, to the expertise available from the faculty.
• Students will have authentic research experience upon graduation.

Reference:

Wieman, Carl. (n.d.) A New Model for Post-Secondary Education, the Optimized University. Retrieived on January 2, 2010, from here.

Think Aloud Protocols

It is possible to study objectively the form of thinking that occurs covertly in many types of typical tasks and activities in everyday life through think aloud or talk aloud protocols (or protocol analysis) (Ericsson and Simon, 1980). One of the biggest obstacles in this type of study is to find nonreactive settings to reproduce the thinking process without altering how the subjects would normally think. The single most important precondition for successful direct expression of thinking is that the participants are allowed to maintain undisrupted focus on the completion of the presented tasks. They should not describe nor explain their thoughts to anyone during the process. Interviewers should also limit their interactions with the subjects as much as possible during the sessions. Subjects can also be given a series of simple warm-up exercises (such as mental multiplication of two numbers) that will provide them with the practice of directing their full attention to the presented task while verbalizing their thoughts.

If participants are asked to describe or explain their thinking, it is found that such verbalizations present "a genuine educational opportunity to make students' reasoning more coherent and reflective" (Ericsson and Simon, 1998). These subjects are more successful in mastering the material and generate more self-explanations and monitor their learning better. Writing is found to be the most effective (as well as demanding) activity to improve and develop student's thinking.

References:

Ericsson, K.A. and Simon, H.A. (1980). Verbal Reports as Data. Psychological Review. 87(3), pp 215 - 251.

Ericsson, K.A. and Simon, H.A. (1998). How to Study Thinking in Everyday Life: Contrasting Think-Aloud Protocols with Descriptions and Explanations of Thinking. Mind, Culture, and Activity. 5(3), pp 178 - 186.

EnGauging Students

EnGauging students is the process of engaging students in learning and gauging what they are learning simultaneously. Engaged students are more motivated to learn, and gauging students in the process provides students with feedback so they know what they need to change in their study habits. Some tools to enGauge students include:

• Brainstorming - list as many answers as possible to a question
• Case studies - solve a problem or situation in a real-world context
• "Clicker" questions - answer questions electronically in class
• Decision making - work together to recommend solutions to a problem
• Group exams - work together to discuss exam questions but writes answers individually
• One-minute papers - write a short answer about a topic or question
• Pre / Post questions - answer questions before and after a topic is taught
• Strip sequence - arrange a series of events into the correct order (e.g. Parson's puzzles)
  
• Think-pair-share - think about possible answers to a question individually, and discuss with partners to come to a consensus
• Reading assessment - enlisting groups of students to design the activities and teach each other
• 99 words / seconds - summarize a topic / lecture in 99 words or in 99 seconds (see example here)
• KWL - have students answer 3 questions, individually or in a group, each class: "what we Know", what we Want to know, and "what we Learned".
  

Reference:

Handelsman, J., Miller, S., Pfund, C. (2007) Scientific teaching. W.H. Freeman & Company.

Desirable Difficulties

Given that the fundamental goal of education is to make changes in the learner's long term memory, Bjork et al. have shown that learning conditions that introduce difficulties for the learners are potent in enhancing long-term retention and transfer. Humans do not simply "store" information in long term memory but rather, we relate new information to what is already known. Our long term memory is not a playback device. It is primarily semantic in nature. "Desirable difficulties" have been shown to be effective in making changes in the long term memory. This includes: spacing rather than massing study sessions, interleaving rather than blocking practice on separate topics or tasks; varying how instructional materials are presented or illustrated; reducing feedback; and using tests rather than presentations as learning events. See Bjork's Seven Study Tips and his slide presentation.

Reference:

Bjork, R. and Linn, M. (ND). Introducing Desirable Difficulties for Educational Applications in Science (IDDEAS). Retrieved on December 3, 2009 from http://iddeas.psych.ucla.edu/IDDEASproposal.pdf.