The current method of modeling the level of understanding students have of course content with a single letter grade is primitive at best. The simplification of weeks of learning into a single character representation does little to convey what a student ultimately knows. Two students who receive the same grade in a course may, and often do, have a very different level of mastery of various course concepts. This paper presents a new paradigm for knowledge modeling and assessment based on concept maps and concept inventories. Under this assessment method, student maps are generated to graphically depict a comprehensive model of student understanding of course concepts. This assessment paradigm begins by creating a comprehensive concept map that depicts each of the relevant concepts and relationships within the topic area being studied. Based upon a concept-inventory-driven analysis of student knowledge, a concept map representing the subset of the comprehensive map that students have mastered is generated as a representation of each student’s knowledge. This paper presents an example of how such an assessment paradigm has been implemented in a mechatronics course unit in a large freshman engineering course. This course unit introduces students to many concepts from electrical, computer, and mechanical engineering. The unit includes an online lecture and a hands-on, lab-based activity in which students build a simple mobile robot. Over 1400 students participated in this course unit in Fall 2008. While the scope of this work is within a single course unit, this paper describes how such modeling can be done on a large scale to represent student knowledge gains in an entire course or even an entire degree program. The methods used for building the comprehensive concept map and an appropriate concept inventory are described. The software developed to generate student maps based on responses to a concept inventory is also discussed. Many applications of this paradigm are described including the use of such assessment methods to augment university admissions data, the ability to replace or augment transcripts and resumes with detailed student maps, the development of college rankings based on student learning outcomes, and objective faculty teaching evaluation based on student learning outcomes.
[1]
D. Ausubel,et al.
Learning theory and classroom practice.
,
1971
.
[2]
D. Hestenes,et al.
Force concept inventory
,
1992
.
[3]
D. Ausubel,et al.
School learning;: An introduction to educational psychology
,
1969
.
[4]
Terry Scott.
Bloom's taxonomy applied to testing in computer science classes
,
2003
.
[5]
C. Lebiere,et al.
The Atomic Components of Thought
,
1998
.
[6]
K. Fisher.
Semantic Networking: The New Kid on the Block
,
1990
.
[7]
M. G. Jones,et al.
The concept map as a research and evaluation tool: Further evidence of validity
,
1994
.
[8]
Josianne Basque,et al.
Collaborative Concept Mapping in Education: Major Research Trends
,
2006
.
[9]
Joseph D. Novak,et al.
Learning creating and using knowledge: Concept maps as facilitative tools
,
1998
.
[10]
Alberto J. Cañas,et al.
A TEORIA SUBJACENTE AOS MAPAS CONCEITUAIS E COMO ELABORÁ-LOS E USÁ-LOS * THE THEORY UNDERLYING CONCEPT MAPS AND HOW TO CONSTRUCT AND USE THEM
,
2010
.
[11]
D. Ausubel.
The psychology of meaningful verbal learning.
,
1963
.
[12]
Benjamin S. Bloom,et al.
Taxonomy of Educational Objectives: The Classification of Educational Goals.
,
1957
.
[13]
Gloria Gomez,et al.
CmapTools: A Knowledge Modeling and Sharing Environment
,
2004
.