Session 1330
HIERARCHY
OF COGNITIVE DOMAIN LEARNING SKILLS
TO GUIDE
ACTIVITY DESIGN, CLASSROOM FACILITATION,
AND CLASSROOM
ASSESSMENT
Steven Beyerlein
and Dan Cordon, Mechanical Engineering
University
of Idaho
Denny Davis,
Bioengineering
Washington
State University
Cy Leise,
Psychology
Bellevue
University
Daniel Apple,
President
Pacific Crest
Abstract
Development of a complex set
of life-long learning skills in the cognitive, social, and affective
domains is an important goal of engineering education. This is complicated
by the reality that learning skill development transcends the temporal
and spatial boundaries of isolated courses (SCANS 1991). This work responds
to the need for a shared language to promote and reinforce learning
skill development between courses and across the curriculum. The research
question that motivated this work is whether greater specificity in
learning skill definition than that prescribed by ABET Criteria 3 and
4 can be a useful tool for daily teaching/learning. This paper
outlines the philosophy, organization, and application of a classification
of learning skills within the cognitive domain. Over 90 distinct
learning skills are grouped into skill clusters that fall within process
areas aligned with Bloom's taxonomy. Learning skills within the classification
apply from pre-college through graduate study. Candidate skills
were inventoried from numerous literature sources and then validated,
positioned, and refined through deliberations of an inter-disciplinary
focus group. This paper includes a holistic rubric for defining, measuring,
and elevating individual learning skills as well as discussion of how
targeting specific skills can strengthen activity design, facilitation
of learning, and classroom assessment.
NEED FOR LEARNING SKILL
CLASSIFICATION
Educators committed to applying
learning theory to classroom practice have long needed a shared language
to use in discussing learning skill development. This is especially
important among faculty engaged in general engineering classes, designers
of active learning curricula, and members of accreditation committees
striving to connect course-level learning outcomes with program-level
outcomes. This paper introduces a framework for understanding
learning skills in the cognitive domain that was initially proposed
by a faculty focus group at Western Michigan University in 2002 and
refined by the authors of this paper. This effort is part of a
larger initiative by inter-disciplinary process educators across the
country to establish a Classification of Learning Skills that provides
a complete set of transferable learning skills in all four domains of human performance—cognitive,
social, affective, and psychomotor (Beyerlein et al., 2003).
The Classification of Learning
Skills is predicated on four findings from pedagogical research.
Learning involves
building a tapestry of conceptual, procedural, and meta-cognitive knowledge
(Bransford et al., 2000).
Learning results
in subject matter mastery, transferable long-term behaviors, and mature
perspectives that can be both measured and elevated (Dewey, 1936).
Subject matter mastery
(conceptual development in an area of knowledge, joined with fluency
in applying it) can be planned, cultivated, and assessed using modern
derivatives of Bloom’s taxonomy (Anderson and Krathwohl, 2001).
Focusing on a small
set of life skills at one time helps learners integrate these skills
into their lives and elevate their daily performance (Covey, 1989).
The early developers of the
Classification of Learning Skills began by recognizing that each discipline
has its own special concepts, tools, language, and performance rubrics.
However, they decided not to attempt a lengthy compilation of many overlapping
skills. Instead, they chose to highlight a smaller listing of
general cognitive skills that appear in multiple learning contexts (Krumsieg
and Baehr, 2000).
The Classification of Learning
Skills embodies a deliberately selective grouping of essential, yet
discrete, learning skills. Each one is assigned only to the domain
where it is most commonly applied; that placement is determined by deciding
where it first becomes most critical to learning performance. While
skills related to thinking processes are housed within the “cognitive”
domain, those related to interpersonal processes can be found under
the “social” domain. Similarly, skills related to attitude
and emotional development are located in the “affective”
domain, and those connected with body development and control, under
the “psychomotor” domain.
The cognitive domain contains
learning skills predominantly related to mental (thinking) processes.
Learning processes in the cognitive domain include a hierarchy of skills
involving processing information, constructing understanding, applying
knowledge, solving problems, and conducting research. These processes
enable performance at five different levels of learner knowledge, originally
suggested by Bloom (1956). The cognitive domain contains several
skill clusters that reinforce key aspects of each of these processes.
Each cluster provides a complete, concise, and complementary listing
of learning skills that are most critical for each process. Selective
attention and sequential development of skills associated with lower
level processes most efficiently leads to mastery of skills associated
with higher level processes. As such, the list of cognitive domain
learning skills contained in this module is a valuable reference for
curriculum design, classroom observation, and assessment of learning
outcomes.
ANATOMY OF A LEARNING SKILL
Learning skills are discrete
entities that are embedded in everyday behavior and operate in conjunction
with specialized knowledge. They can be consciously improved and
refined. Once they are consciously recognized, the rate and effectiveness
of overall learning increases. They can be identified at an early
stage of a learner’s development. No matter what the person’s
age or experience, learning skills can be improved to higher levels
of performance through self-reflection, self-discipline, or guidance
by a mentor. This growth in learning skill development is usually
triggered by a learning challenge of some kind and is facilitated by
actions built on a shared language between mentor and mentee.
Finally, growth and development of a learning skill is sustained by
quality feedback. These factors underlie the rubric for learning
skill development presented in Table 1. Note how these change
incrementally as one progresses from the rudimentary (Level 1) to the
sophisticated (Level 5).
Table 1.
Illustration of Cognitive Domain Competency Levels
Level
of Competency
Description of
Competency Level
Examples:(a)
Listening
(b)
Identifying assumptions
Level
5
Transformative use
Skill is expanded and integrated with
other skills for creative, productive application in novel contexts;
inspires others to emulate use
(a) Purposefully listens and observes
nuances and contextual details that deepen understanding of information
and its application to a clearly stated need
(b) Clearly articulates own and others’
assumptions, enabling all to understand impacts on interpretations and
conclusions on matters involving a wide variety of disciplines and
perspectives
Level 4
Self-reflective use
Effective use of skill by learner; skill
can be self-improved and adapted to unfamiliar contexts with occasional
advice from a mentor
(a) Carefully listens and reflects on
success to gain maximum understanding relevant to a specific need
(b) Analyzes and recognizes relative
impacts of assumptions made by self and others across a variety of disciplines
and perspectives
Level 3
Consistent performance
Skill used routinely and effectively
in multiple contexts through learner self-direction; not able to advance
without external coaching
(a) Carefully listens to understand
key points useful to address a specific need
(b) Looks for impacts of assumptions
by self and others in discussing interpretations and conclusions within
areas of specialty
Level 2
Conscious use
Skill used knowingly, possibly proactively,
by learner, but skill needs to be constantly challenged by a mentor
(a) Actively listens; identifies information
thought important to general need
(b) Aware of some assumptions underlying
personal interpretations and conclusions, but often unaware of assumptions
made by others
Level 1
Non-conscious use
Use of skill initiated by a prompt or
influence external to the learner; unintended use of skill
(a) Passively listens; notes only information
highlighted by others
(b) Unaware when assumptions are made
by self or others, often leading to erroneous conclusions
Two different learning skills
from the cognitive domain are analyzed in Table 1—listening and identifying
assumptions. These two examples illustrate how a specific skill
used for basic processing of information and another skill used in constructing
understanding can be demonstrated at very low levels (without conscious
effort) and at very high levels (impressing and inspiring others). Monitoring
learning skill proficiency along a common developmental continuum can
be a tremendous motivator for learners. Similarly, recognizing
which skills are underdeveloped in different learning situations can
be used to plan interventions that accelerate desired cognitive development.
The Classification of Learning
Skills is based on three assumptions:
By focusing on a
small set of transferable, mutually exclusive learning skills, educators
have an opportunity to build shared language about learning performance.
Admittedly, there are many more learning skills than those featured
in the Classification. In addition, the labels educators use to
describe these often differ from one person to the next and from one
discipline to the next. So, in order to work more productively
across classroom and temporal boundaries, it is helpful to have a broadly
recognized system for naming these skills.
A rubric for learning
skill development helps educators and learners to understand and assess
individual skills. However, it is important to keep in mind that
learning skills are developed through practice and feedback; they cannot
be elevated through conceptual knowledge alone.
A person only recognizes
the need to learn a new learning skill when he or she cannot perform
a task at a certain level—in other words, when the current skill level
is less than that required for the task. If the learner perceives a
task to be less challenging than his or her level of competence, they
will not seek higher-level skills to do it.
ORGANIZATION OF THE COGNITIVE
DOMAIN
The Cognitive Domain encompasses
thinking skills that are independent of context and discipline.
In contrast to other domains of learning, the cognitive domain addresses
development that is individual rather than interpersonal, focuses on
content rather than context, and is independent of emotion. The
skill listing given in Figure 1 includes over 90 transferable learning
skills relevant to undergraduate education, graduate education, and
professional practice (Davis, 2003). These learning skills were
worded in a manner intended to appeal to users in all academic disciplines.
Enough specificity has been retained to insure that well-defined cognitive
domain learning skills can be traced to most course and program learning
outcomes.
Cognitive skill development
is logically sequenced following the levels of Bloom’s Taxonomy because
learning skills from lower-level processes are embedded in learning
skills associated with higher-level process (Anderson & Krathwohl,
2001). Five thinking processes therefore comprise the cognitive
domain. These are: (1) Processing Information, (2) Constructing
Understanding, (3) Applying Knowledge, (4) Solving Problems,
and (5) Conducting Research. Critical thinking is purposely
not identified with a single process area in the cognitive domain.
Instead, critical thinking is considered a super-process that draws
from all process areas in the cognitive domain during the creation of
new knowledge or the improvement of existing knowledge. This viewpoint
is consistent with principles of the National Council for Excellence
in Critical Thinking (Paul, 2003).
Within each process area, learning
skills are divided into clusters. Unlike the process areas, the
skill cluster associated with a particular process area and the specific
skills associated with each cluster do not follow a hierarchy.
Skill clusters are given labels that communicate their role within each
process area. For example, Processing Information includes
the skill clusters collecting data, generating data, organizing data,
retrieving data, and validating information. In Figure 1, processes
are shown in bold, skill clusters are shown in bold italics, and learning
skills as well as their definitions are shown without special formatting.
Processing Information
Collecting Data
(from disorganized source)Retrieving Data (from organized source)
Observing – seeing details in an environment/object
Recognizing patterns – perceiving consistent repetitive occurrences
Listening
– purposeful collection of aural data Searching – locating
information within a system
Skimming
– inventorying using key prompts Recalling – retrieving from
memory
Memorizing
– active mental storage of information Inventorying- retrieving
from collective memory
Recording
– transcripting key information
Measuring
– obtaining data using a predetermined scale
Generating Data
(to fill a void)Validating Information (for value)
Predicting
– forecasting from experience Testing perceptions – verifying
based in interpretations
Estimating
– approximating from mathematical models Validating sources
– verifying based on credibility
Experimenting
– inferring from empirical study Controlling errors – verifying
based on procedures
Brainstorming
– gathering ideas from previous experience Identifying inconsistency
– detecting outliers/anomalies
Ensuring sufficiency – verifying
data quantity/quality for context
Organizing
Data (for future use)
Filtering – selecting data based on criteria
Outlining
– identifying primary and subordinate groupings
Categorizing
– associated data with established groups
Systematizing
– designing an organizational framework
Constructing Understanding
Analyzing (characterizing
individual parts)Reasoning (revealing meaning)
Identifying
similarities – recognizing common attributes Interpreting –
bringing meaning for better understanding
Identifying
differences – recognizing distinguishing attributes Inferring
– drawing conclusions from evidence and logic
Identifying
assumptions – examining preconceptions/biases Deducing – arriving
at conclusions from general principles
Inquiring
– asking key questions Inducing – arriving at general principles
by specific instances
Exploring
context – seeing relationship of parts to environment Abstracting
– describing essence of an idea, belief, or value
Synthesizing (creating
from parts)Validating Understanding (for reliability)
Joining – connecting identifiable parts Ensuring compatibility
– testing consistency with prior knowledge
Integrating
– combining parts into new whole Thinking skeptically – testing
against fundamental principles/schema
Summarizing
– representing whole in a condensed statement Validating completeness
– checking for missing aspects
Contextualizing
– connecting related parts to environment Bounding – recognizing
limits of application of knowledge
Applying Knowledge
Performing with
Knowledge (in real context)Being Creative (in new contexts)
Clarifying
expectations – defining proficiency level Challenging assumptions
– exploring possibilities by relaxing constraints
Strategizing
– planning how to use knowledge Envisioning – imagining desired
conditions
Using prior
knowledge – integrating unprompted knowledge Linear thinking
– generating new ideas from previous ideas
Transferring
– using ideas in a new context Divergent thinking – taking
variety of positions to stimulate ideas
Transforming
images – manipulating images to gain new insight
Lateral thinking – generating
new ideas from associations
Modeling (in abstract
context)Validating Results (for appropriateness)
Analogizing –
representing similar elements in new contexts Complying – comparing
results with accepted standards
Exemplifying
– showing by example Benchmarking – comparing with results
from best practice
Simplifying
– representing only primary features Validating – using alternative
methods to test results
Generalizing
– transferring knowledge to multiple contexts
Quantifying
– representing with numbers or equations
Diagramming
– clarifying relationships through visual representation
Figure 1.
Cognitive Domain Learning Skills (taken from Davis, 2003)
Solving Problems
Identifying the Problem (to establish focus)Creating Solutions (for
quality results)
Recognizing
the problem – stating what is wrong or missing Reusing solutions
– adapting existing methods/results
Defining
the problem – articulating a problem/need for solution Implementing
– executing accepted solution practices
Identifying
stakeholders – naming key players/audiences Choosing alternatives
– selecting alternatives using criteria
Identifying
constraints – recognizing limitations to solutions Harmonizing
solutions – fitting components into holistic solution
Identifying
issues – inventorying key stakeholder desires/concerns
Structuring the
Problem (to direct action)Improving Solutions (for greater impact)
Categorizing
issues – grouping by underlying principles Generalizing solutions
– modifying for broader applicability
Establishing
requirements – articulating solution criteria Ensuring robustness
– modifying to fit more contexts
Subdividing
– separating into sub-problems Analyzing risks – identifying
external sources/impacts of error
Selecting
tools – finding methods to facilitate solution Ensuring value
– testing against requirements and constraints
Conducting Research
Formulating Research
Questions (to guide inquiry)
Locating relevant literature – searching
out seminal sources
Identifying
missing knowledge – determining gaps in community understanding
Stating
research questions – asking empirically answerable questions
Estimating
research significance – forecasting value/impact to community
Writing
measurable outcomes – specifying deliverables from research
Obtaining Evidence
(to support research)
Designing
experiments – specifying observable parameters and sampling
Selecting
methods – determining research procedures
Extracting
results – analyzing data to produce quality characterizations
Replicating
results – duplicating experiments and findings
Discovering (to
expand knowledge)
Testing
hypotheses – discerning significant effects
Reasoning
with theory – explaining data with accepted knowledge
Constructing
theory – formulating new conceptual structures
Creating
tools – adapting knowledge for practitioners
Validating Scholarship
(for meaningful contribution)
Defending
scholarship – presenting within disciplinary performance expectations
Responding
to review – elevating scholarship from community input
Confirming
prior work – adding credibility to body of knowledge
Judging scholarship
– evaluating scholarship against criteria
Figure 1.
Cognitive Domain Learning Skills – cont’d (taken from Davis,
2003)
SELECTION AND PLACEMENT
OF LEARNING SKILLS
Each of the skill listings
in the Classification was brainstormed, located, and validated by several
cross-disciplinary teams consisting of a half-dozen faculty members
working in a Pacific Crest faculty institute held at Western Michigan
University. This typically began by writing short definitions of potential
(“candidate”) skills that were then placed within a process area
and assigned key attributes.
To be considered for the classification,
each learning skill was then tested against all of the following criteria:
improvement in this
skill leads to enhancement of learning performance,
the skill is accessible
and usable at all times,
performance in this
learning skill is unbounded (i.e., can be “grown” to progressively
higher performance levels),
the skill is transferable
across disciplines and contexts,
the skill applies
to multiple forms of knowledge,
the skill is a holistic
element which can not be subdivided (i.e., it can not be either a label
for a cluster of skills or a label for a process), and
the skill is not
a process consisting of multiple steps.
Once a skill passed all of
the above tests, it was associated with a predominant domain and linked
with the appropriate skill cluster. The skill cluster was then examined
to ensure that it formed a compact, complete, and non-overlapping set—in
other words, that nothing essential was left out or shared with another
cluster. In this process, the following conditions had to be met:
each of the skills
is distinct and provides unique added value to the set;
the skills and
definitions are worded concisely, congruently, and completely; and
the skills are not
critical to learning performance at the next lower process level.
As “candidate” skills were
considered for the Cognitive Domain Classification, definitions were
refined so that they represented something unique and essential.
This continued until all redundant learning skill components had been
parsed out and nothing new remained. The skills and clusters were further
refined by the authors subsequent to the initial focus group meeting.
Using the COGNITIVE DOMAIN
By incorporating the transferable
learning skills into instructional design and delivery, process educators
have experimented with ways to make subject matter mastery more authentic
(Hanson and Wolfskill, 2000).
The Cognitive Domain offers a tool for highlighting and measuring well-defined
subsets of learning skills traditionally associated with course content.
Three examples are given in this section how learning skills in the
Cognitive Domain can be used to enhance activity design, classroom facilitation,
and classroom assessment.
Activity Design: Figure
2 portrays a learning activity that encourages design teams at any level
to explore a wider variety of methods for creative thinking in pursuing
innovative design solutions. Teams are first asked to list as
many methods as they can to generate creative ideas for design solutions.
Next they are given the listing of Cognitive Domain learning skills
and asked to analyze the skills under the creativity cluster.
They are then challenged to apply each of the creative thinking skills
to generate one or more ideas for the problem of fastening two pieces
of paper together. Table 2 gives a result from this task.
Next, students are asked to select a difficult area of their design
project calling for creativity and to apply each of the creative thinking
skills to generate a design alternative. Finally, they are asked
to discuss how they might systematically add creativity to their design
team efforts in order to produce a higher quality design solution.
In subsequent project meetings teams are encouraged to use the learning
skills from the creativity cluster as prompts to improve brainstorming
and decision-making. By introducing a common language for discussing
and self-assessing creativity skills, design teams can better collaborate
in elevating these skills. Through consciously challenging assumptions,
envisioning, linear thinking, divergent thinking, transforming images,
and lateral thinking, design teams are also likely to surface more and
better ideas for their projects.
Developing Skills for Increased Creativity
Outstanding design solutions
have a discernable element of creativity. Design teams with broad
skills in creativity have resources to tap for generating more innovative
solutions that have a greater likelihood of meeting customer needs.
In this activity your team will identify, explore, and enhance several
skills for creative thinking. You will be given a language for
discussing and reflecting on elements of creativity that can benefit
idea generation in your design projects.
Objective:
Explore a variety of methods
for creative thinking to expand your team’s tool set for generating
innovative design solutions.
Criteria for Success:
Teams are able to
recognize and utilize new skills for creating ideas.
Teams agree on an
action plan for improving productivity in future idea generation.
Tasks:
Assign roles to
support this activity. (one minute)
Without looking
ahead, list as many methods as you can to generate creative ideas for
design solutions. (four minutes)
Review the Cognitive
Domain skills for creative thinking. Use each skill to create
a second idea for attaching two sheets of paper together. (ten minutes)
In an area of your
project calling for creativity, apply each of the creativity skills
to generate one or more new ideas. Record these and be prepared
to report on these at the end of the activity. (ten minutes)
Identify three ways
to systematically add creativity to your team’s efforts. (five
minutes)
Deliverables:
Team Report presents:
- Ideas generated in response to a project need
- Strategy
to improve team creativity
Team Reflector reports:
- A team strength for this activity
- An area in which the team can improve
- An insight about creativity
Figure 2.
Creative Thinking Activity
Table 2. Results from Creative Thinking Activity
Cognitive
Learning Skill (related to creativity)
Prompt
Idea
Challenging
Assumptions
Remove assumption: papers
need not be in same plane
Slit both pieces and insert
into one another in shape of “T”
Envisioning
Ideal conditions: No extra
volume required by fastener
Use static charges to hold
pieces together
Linear
Thinking
Previous idea: staple
Rivet together
Divergent
Thinking
Opposite position: hold papers
apart
Slide object between the pieces;
slide another object outside both pieces
Transforming
Images
Image: papers floating through
air while together
Use flow of air between papers
to draw them together
Lateral
Thinking
Illogical association: paper
heavy enough that gravity holds top sheet down
Add substance to paper to
increase its weight
Classroom Facilitation:
One of the more valuable processes to help faculty improve their performance
in the classroom is peer coaching. The peer coaching process has
three stages. First, a faculty member desiring to improve some
aspect of their classroom performance, requests another faculty member
to give feedback on what they see. There is a dialog prior to
the classroom session where the focus for peer coaching is explicitly
identified. Learning skills can be used to add specificity to
this assignment. Second, the peer coach spends time in class collecting
data on student performance and instructor interactions along with analysis
of strengths, improvements, and insights. The strengths should
describe why a particular behavior is valuable and hypothesize what
caused this behavior. The improvements should offer suggestions
for implementation as well as context in which these are appropriate.
The insights should be transferable across disciplines and teaching
environments. Third, the peer coach sits down with the instructor
after the classroom session to give feedback on what they saw and to
answer clarifying questions. While the primary purpose of peer
coaching is helping the instructor improve their facilitation skills,
a second benefit is helping the peer coach see ways in which to improve
their own classroom performance.
The peer coaching form shown
in Figure 3 is intended to structure class observation by a peer coach
and to capture data for fruitful post-class discussion. It begins
with a forecast of key learning skills for a particular lesson that
has well-defined content. It focuses on just a few skills and seeks
to collect insights on how well students are using these skills and
how effectively the instructor intervenes on these skills in the process
of achieving content objectives. Subsequent questions in Figure
3 are intended to stimulate and enrich post-class dialogue. Note
that these questions can be used to explore facilitator performance
in the social and affective domains as well as the cognitive domain.
Peer Coaching Form
Class/Period:Date:
Instructor/Assessee:Peer/Assessor:
Which three learning
skills does the instructor consider most critical to the success of
this activity? Why are these particular skills important?
What difficulties are students likely to encounter because of skill
deficiencies?
How were students
made aware of targeted learning skills during activity start-up?
Which three learning
skills were most highly developed by the student teams you observed?
What circumstances surrounded the application of each skill?
What other learning
skills could the facilitator have focused on during the activity?
(i.e. lost opportunities)
How effectively
did the facilitator use peer and self-assessment to improve awareness
and use of key learning skills?
Which two interventions
by the facilitator were most effective in helping students improve their
skills and how were these done? What improvements would you recommend?
Figure 3.
Peer Coaching Report on Learning Skills
Classroom Assessment:
In teaching a process, such as engineering design, it is instructive
to identify critical skills at each stage of the process for students
to address in elevating their performance. What skills are critical
in the process can change with students’ experience and developmental
level. Table 3 portrays a comparison of learning skills associated
with engineering design in two contexts that represent different levels
of development: (a) freshman engineering design and (b) senior capstone
engineering design. Skills are grouped around seven major elements of
the design process described on the Transferable Integrated Design Engineering
Education (TIDEE) web site: www.tidee.cea.wsu.edu. Elements of this process listed
in column 1 include: information gathering, problem definition, idea
generation, evaluation and decision making, implementation, and process
improvement.
The second column of Table
3 relates to the PET bottle activity that is used in an introduction
to engineering class. Freshman design teams are asked to propose
uses for empty PET bottles that maximize use of material while at the
same time creating a valuable consumer product. Cluster labels
from the cognitive domain are shown in bold italic. Likewise,
italics identify cognitive domain learning skills. Note that there
are some additional learning skills outside the cognitive domain that
are identified at the bottom of the table.
The third column in Table 3 relates to a design review for an industry-sponsored
senior design project. Skills in this column are more complex
than those associated with the freshman class because senior students
are assumed to be at a higher level of development. The comparison of
skills in these two columns serves as a prompt to instructors and students
to develop and expect demonstration of higher level skills in the senior
course.
The skills listed in Table
3 can serve as a guide for classroom assessments in design courses.
It is not necessary to address all of the enumerated skills in a single
performance assessment, only those skills that are most prominent or
most deficient. By focusing on specific skills within a process,
like design, students can become more metacognitive about their use
of these skills and can better visualize strategies for strengthening
the design process based on improved performance in a manageable set
of skills.
CONCLUSIONS
The Cognitive Domain is part
of a learning skill classification that addresses cognitive, affective,
social, and physical dimensions of learning. This hierarchy of
learning skills is a useful tool for facilitating learners’ growth
and development, measuring and documenting growth, assessing self and
peer performance, and improving instructional design for skill development.
It aligns with accepted learning theories that consider both learning
skill development and subject-matter mastery. It is built around
a common model for learning skill development shown in Figure 1.
The Cognitive Domain skill definitions stimulate new kinds of questions
related to the identification and understanding of learning skills.
These include measurement of learners’ growth and development, the
role of faculty in mentoring, and the importance of skill development
to learning (as opposed to exclusively focusing on content mastery.)
Teachers and learners need
to understand the hierarchy of processes and skills within the Cognitive
Domain so they appreciate prerequisite skills for learning as well as
the way these skills need to be transformed to master more complicated
elements of discipline-specific concept inventories. Development
of learning skills should never be taken for granted in teaching or
learning new content. Skills associated with lower-level processes
should be introduced in foundational courses and elevated in intermediate-level
coursework. Skills associated with higher-level processes should
be thoughtfully introduced and reinforced in upper-division courses.
Methodically invoking key learning skills from different process areas
and clusters across the cognitive domain also provides a method for
infusing richness in course activities while strengthening life-long
learning skills. The Cognitive Domain presented here also serves
to remind us that improved cognitive domain performance is always possible,
no matter what one’s state of learning skill development.
Table 3. Comparison of Skills
for Different Development Levels in Engineering Design
Elements
of Design
Skills
for Freshman Design
Skills
for Senior Design
Information
Gathering
Collecting Data
Observing
Listening
Recording
Analyzing
Inquiring
Exploring
context
Identifying the
Problem
Identifying
stakeholders
Recognizing
the problem
Analyzing
Exploring
context
Identifying
assumptions
Validating Information
Validating
sources
Identifying
inconsistency
Problem
Definition
Identifying the
Problem
Identifying
assumptions
Establishing
requirements
Synthesizing
Summarizing
Integrating
Modeling
Quantifying
Diagramming
Identifying the
Problem
Identifying
constraints
Identifying
issues
Structuring the
Problem
Establishing
requirements
Categorizing
issues
Idea
Generation
Generating Data
Brainstorming
Experimenting
Being Creative
Challenging
assumptions
Divergent
thinking
Lateral thinking
Formulating Research Questions
Locating
relevant literature
Identifying
missing knowledge
Being Creative
Envisioning
Challenging
assumptions
Evaluation
and Decision Making
Organizing Data
Systematizing
Categorizing
Modeling
Exemplifying
Simplifying
Obtaining Evidence
Selecting
methods
Designing
experiments
Creating Solutions
Reusing
solutions
Choosing
alternatives
Harmonizing
solutions
Implementation
Performing with
Knowledge
Clarifying
expectations
Strategizing
Reasoning
Inferring
Deducing
Structuring the
Problem
Subdividing
Selecting
tools
Discovering
Reasoning
with theory
Testing
hypotheses
Process
Improvement
Validating Information
Testing
perceptions
Controlling
errors
Validating Understanding
Ensuring
compatibility
Validating
completeness
Bounding
Validating Results
Complying
Benchmarking
Improving solutions
Ensuring
robustness
Analyzing
risks
Ensuring
value
Team
Dynamics
Respecting
other’s ideas
Playing
assigned roles
Committing
to hard work
Achieving
consensus
Recording
activity
Committing to team goals
Accepting accountability
Supporting team members
Client Communication
Documenting Progress
REFERENCES
Anderson, L.W. & Krathwohl,
D.R. (Eds.). (2001). A taxonomy for learning, teaching and
assessing. New York: Longman.
Beyerlein, S., Leise, C., Baehr,
M., and Apple, D. (2003). Faculty Guidebook Series: Classification
of Learning Skills. Lisle, IL: Pacific Crest.
Bloom, B.S. (1956). (Ed.).
Taxonomy of educational objectives: The classification of educational
goals (Handbook 1: Cognitive domain). New York: McKay.
Bransford, J.D., Brown, A.L.,
Cocking, R.R., & Pellegrino, J.W. (Eds.). (2000). How people
learn: Brain, mind, experience, and school. Washington, DC: National
Academy Press.
Covey, S. (1989). Seven
habits of highly effective people. New York: Simon & Schuster.
Davis, D., Beyerlein, S., Leise,
C., and Apple, D. (2003).
Faculty Guidebook Series: Cognitive Domain Module. Lisle,
IL: Pacific Crest.
Dewey, J. (1938). Experience
and education. New York: MacMillan.
Engineering Accreditation Commission.
(2004). Engineering Criteria, Accreditation Board for Engineering
and Technology, Inc., Baltimore, MD.
Hanson, D. and Wolskill, T.
(2000). Process Workshops—a new model for instruction. Journal
of Chemical Education, 77, 120-130.
Krumsieg, K., and Baehr, M.
(2000). Foundations of learning. Lisle, IL: Pacific Crest.
Paul, R. (2003). National
Council for Excellence in Critical Thinking. Draft statement of
principles. www.criticalthinking.org/ncect.html.
Secretary’s Commission on
Achieving Necessary Skills (SCANS). (1991). What Work Requires of
Schools: A SCANS Report for America 2000. Department of Labor.
TIDEE. (2004). Transferable
Integrated Design Engineering Education. www.tidee.cea.wsu.edu.
AUTHOR BIOGRAPHIES
STEVEN BEYERLEIN
Steven Beyerlein is professor
of Mechanical Engineering at the University of Idaho, where he coordinates
the capstone design program and regularly participates in ongoing program
assessment activities. For these efforts he won the UI Outstanding
Teaching Award in 2001. He received a Ph.D. in M.E. from Washington
State University in 1987. His research interests include catalytic
combustion systems, application of educational research methods in engineering
classrooms, and facilitation of professional development activities.
DAN CORDON
Dan Cordon is a doctoral candidate
in Mechanical Engineering at the University of Idaho. His research
focuses on engine and vehicle development promoting improved efficiency
and reduced emissions through the use of alternative fuels and catalytic
ignition systems. He maintains an active interest in incorporating
student centered learning practices in running the engine research facility.
This is used to modify and calibrate engines used in Future Truck, Formula
SAE, and Clean Snowmobile Competitions. For his efforts in this
area he received a University Transportation Centers Student of the
Year award in 2004.
DENNY DAVIS
Denny Davis is professor of
Biological Systems Engineering at Washington State University and Director
of the Transferable Integrated Design Engineering Education (TIDEE)
project, a Pacific Northwest consortium of institutions developing improved
curriculum and assessments for engineering design education. Dr.
Davis teaches and assesses student learning in multidisciplinary capstone
design courses. He is a Fellow of ASEE.
CY LEISE
Cy Leise is professor of Psychology
at Bellevue University near Omaha, Nebraska. He is chair of his
department and director of the Master’s program in Human Services.
He earned his doctoral degree in Educational Psychology from the University
of Nebraska-Lincoln in 1981. Dr. Leise is a faculty leader for
program assessment and for learning-oriented curriculum improvement
at Bellevue and has written extensively on learning and mentoring topics
in the Pacific Crest Faculty Guidebook. Current interests include
active learning, for which he has created rubrics for both student and
faculty self-assessment, and redesign of the introduction to psychology
course into a discovery-based learning format.
DANIEL APPLE
Daniel Apple is president of
Pacific Crest Software, an educational consulting company that conducts
faculty development workshops and develops curriculum material for active
learning.
Over the last ten years, he
has collaborated with over 1000 faculty members in improving their curriculum
design, facilitation, and assessment skills. The Classification
of learning skills referenced in this paper was an outgrowth of numerous
focus group sessions focused on transferring educational research findings
to classroom practice and facilitated by Dr. Apple.
Delete the word
“all” because the swpiritual domain is omitted.
Is this 2004?
Need skills
for this level.
2004?
Proceedings
of the 2004 American Society for Engineering Education Annual Conference
& Exposition
Copyright
@2004, American Society for Engineering Education