| • | identify concepts that were not clearly presented in lecture |
| • | influence future lectures and section material based on this information |
| • | identify students who need help and what they need help in |
| • | improve the dialog between the students and teaching team without requiring significant additional effort on either part |
These objectives are akin to the teaching objectives of any professor regardless of class size; however in large lecture classes, the vast number of students forces a long time delay that disrupts the information feedback cycle. Information collection and evaluation (i.e. homework, quizzes) has typically required effort that scales with class size. To address these issues, we chose to implement a computer mediated system to distribute assignments to students, assess their learning, and report these results to the teaching team instantaneously.
The system takes a two step approach towards distilling then evaluating student learning. The teaching team is required to provide sets of questions designed to specifically to assess students learning (rather than practice). For automatic scoring to be accomplished, these questions must be of multiple choice or true/false format. In addition to selecting a response, students are required to submit textual justification for their answers. Students are allowed to access and update their answers up to the due time. Once the assignment comes due, students receive their scores and are provided with solutions for review.
The teaching team interface provides access to student answers through several mechanisms. Histograms of class performance show overall class performance and performance by discussion section. These histograms are 'live' in that by selecting a bar for a question, the teaching team can 'drill down' into a histogram of answers. Selecting an answer bar provides a list of students who chose that answer and their justifications. These too are 'live' in that the teaching team is able to send e-mail to the student simply by selecting their response. A student's response is also linked to all of their responses for the assignment, allowing the instructor to evaluate the pervasiveness of student misperceptions.
Assignment questions are also sorted into a list of frequently missed questions (FMQs) which reveal concepts that the class as a whole did not understand (or poor questions). The same histogram mechanism is available to allow the teaching team to 'drill down' into collective responses for a question to try and understand patterns of misconception.
A third interface sorts data by student score of those who have done poorly on an assignment and tracks consistently low scores throughout the class. Selecting
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Questia, a part of Gale, Cengage Learning. www.questia.com
Publication information:
Book title: Human-Computer Interaction:Communication, Cooperation, and Application Design.
Contributors: Hans-Jörg Bullinger - Editor, Jürgen Ziegler - Editor.
Publisher: Lawrence Erlbaum Associates.
Place of publication: Mahwah, NJ.
Publication year: 1999.
Page number: 736.
This material is protected by copyright and, with the exception of fair use, may not be further copied, distributed or transmitted in any form or by any means.
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