Developing Concrete Research Proposals and Facilitating Self-Directed Learning via Concept Mapping

doi:10.4236/ce.2013.42016

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Creative Education

2013. Vol.4, No.2, 110-116

Published Online February 2013 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2013.42016

110

Developing Concrete Research Proposals and Facilitating

Self-Directed Learning via Concept Mapping

Keetam D. F. Alkahtani

Department of Special Education, College of Education, King Saud University, R iyadh, KSA

Email: [email protected]

Received November 8th, 2012; revised December 10th, 2012; accepted December 24th, 2012

Self-direction in learning and writing viable research proposals are considered by higher education insti-

tutions as essential skills for graduate students to start their careers as researchers. This is an evi-

dence-based practice study focuses on the use of concept mapping to facilitate self-directed learning and

enhance research proposal writing in teacher education. An action mixed methods research design was

used in this study with quantitative and qualitative data. Participants were 29 graduate students who were

enrolled in a core course aimed to provide learners with an in-depth understanding of research methods.

All students, at the beginning of the course, were asked to write a research proposal and complete the

Self-Directed Learning Readiness Scale (SDLRS). They then were given training in the use of concept

mapping technique throughout the course to develop research proposals. Students’ scores prior to and af-

ter the intervention were compared. Results indicate that students developed significantly more concrete

research proposals, and displayed higher scores at post-intervention assessment. Findings of this study

value and support the use of concept mapping to provide students with a comprehensive understanding of

the knowledge of their area of study as they reflect on every element of their proposals.

Keywords: Concept Mapping; Meaningful Learning; Self-Directed Learning; Research Proposals; Action

Research; Higher Education

Introduction

Conceptual Framework

We usually are unaware of our beliefs, values, and assump-

tions about how knowledge is created. Objectivity cannot be

isolated from subjectivity, and the connection between them

constructs our knowledge. Thus, we learn through collective

reflection and action (Gee, 2005; Novak, 1998; Roberts & Dick,

2003). Authority gaps between educators and students might

lead students to undervalue their own knowledge and abilities.

Narrowing gaps in authority elevate the worth of students’ in-

vestigative skills, practical and experiential knowledge. This

assumption is in line with action research philosophy which

views the research as shared ownership and the researcher, or

research team, should not work for or about the participants, but

rather with them (Denzin & Lincoln, 2005). Participants, there-

fore, co-construct knowledge through shared experience and

reflection (Roberts & Dick, 2003). I view action research,

which is the method that I adopted for this study, as a process

that pay attention to issues of experiential knowledge and em-

powering participants to change some part of their circum-

stances or experiences for the better. Eventually, as a teacher-

action researcher, this will led me to better understand and im-

prove my practice. It will also empower my students to exam-

ine their knowledge and gain a sense of their own understand-

ing and needs as they learn. Critical constructivist paradigm is

the philosophy that I have adopted in this research. This para-

digm theorizes that all knowledge is socially constructed and

our knowledge is co-constructed by our understanding and

experiences with the social world. Thus, there is neither one

truth, nor one knowledge (Bergman, 2008; Carr & Kemmis,

1983; Charmaz, 2006). The idea of multiple realities, truths and

ways of knowing was a key in the development of this research.

Research Purpose

Teaching a course of research design is challenging. Most of

my students, even when they have the ability to write a good

course paper, they lack the ability of writing a clear, structured,

articulate and persuasive proposal. Writing a viable research

proposal requires knowledge, wisdom, and reflective thinking.

The state of my students’ learning is best described in one of

Eliot’s most famous quotes “Where is the life we have lost in

living? Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in information?” The

quote portrays the relationship between life, wisdom, knowl-

edge, and information. This relation is the origin of the data-

information-knowledge-wisdom hierarchy (also known as the

DIKW pyramid). According to the DIKW sequence informa-

tion is build from data. Yet, information is not a collection of

data. Attributing meaning to data within context will be crucial

to translate data to information. Understanding of the relations

between data provides the foundation of information needed to

answer who, what, where, and when questions. Knowledge is

constructed from information, but it is not a collection of in-

formation. Relations between information can be stated in pat-

terns which have the potential to represent knowledge. Under-

standing of the relations between patterns is fundamental to

understand those patterns and generate knowledge that can be

used for answering how questions. Wisdom arises from knowl-

edge, but it is something much more than a collection of

knowledge. Understanding of the founding principles for the

K. D. F. ALKAHTANI

patterns is a vital step towards achieving wisdom that will serve

in answering why questions (Ackoff, 1989; Bellinger, 2004;

Rowley, 2007; Zins, 2007). Meaningful knowledge is required

to evaluate understanding and grasp the truth which is the value

we extract from wisdom. Meaningful knowledge can be ob-

tained via meaningful learning (Novak, 1998). In this action

research, I utilize self-directed learning (SDL) and conceptual

approach in conjunction with concept maps to endorse mean-

ingful learning. Promoting the development of meaningful

learning is crucial to enjoy and understand research process.

Meaningful learning might lead my students to a deeper under-

standing and possibility of benefit from the experience of doing

research. As my students are beginning their journey into the

research arena, self-directedness in learning and knowing more

about their own knowledge construction process can have a

major impact on their ability and skills as researchers. The

purpose of this research is to promote dialogue among graduate

faculty to better understand the problems graduate students

have with writing viable research proposals, and to investigate

some practical solutions. The overarching research question

used to frame this action research was: Do using concept map-

ping promote self-directed learning (measured by Self-Directed

Learning Readiness Scale, Guglielmino and Associates, 2004)

and influence the production of a viable research proposal?

Self-Directed Learning

In learning, self-direction has two aspects. The first is as a

goal of education. Individuals are not equally self-directed.

Educational practice should be designed to develop or enhance

the learner’s ability to be self-directed. Developing self-direc-

tion is essential for success in understanding one’s own learn-

ing needs, as well as identifying goals for the learning and the

proper resources needed to accomplish these goals (Candy,

1991). Therefore, individual’s ability to be self-directed assists

in the natural progression of learning as lifelong quest for

knowledge which is a highly desirable goal of education.

The second aspect of self direction is as method of learning.

In this method of learning, the learner willingly teaks higher

levels of responsibility and control over her own learning ex-

perience and eventually will arrive at a point where her learning

is completely independent (Candy, 1991; Knowles, 1975).

There are three basic models of how to develop self direction as

method of learning: linear, interactive, and instructional. The

linear model (Knowles, 1975) is very linear in nature and in-

volves steps in a linear process. The teacher must let the learner

assume most of the responsibility for planning and completing

the learning task and learn at her own pace (Knowles, 1975).

The interactive model (Garrison, 1997) is based on the premise

that the natural state of learning is through inquiry and does not

always follow a set pattern. The teacher provides guidance with

locating or organizing resources and assessing learning. How-

ever, the achievement of specific goals by planning, imple-

menting, completing, and evaluating the learning task is the

learner’s primary responsibility (Garrison, 1997). The instruc-

tional model (Grow, 1991) consists of instructional methods

and assignments which can be incorporated into the learning

environment to aid the learner in becoming more self-directed.

Learners, according to this model, can be identified as: De-

pendent learners (play a very passive role in their learning, and

fully dependent on the teacher), Learners of moderate self-

direction (willing to take part in their learning), Learners of

intermediate self-direction (have the ability to perceive them-

selves as active participants in the process of their learning),

and learners of high self-direction (have the ability, with little

or no help from a facilitator, of planning, carrying out, and

evaluating their own learning) (Grow, 1991). In these three

models, regardless of their structure, the major goal is to aid

learners in becoming more self-directed and the role of the

teacher is a facilitator of the learner’s learning experience.

Building on the promising practices of the literature, my pri-

mary role as a teacher-action researcher was providing my stu-

dents with the skills necessary to plan, implement, and assess

their own learning.

Meaningful Learning and Concept Mapping

Ausubel’s assimilation theory focused on the process through

which humans acquire knowledge. The main notion of this

theory is centers on representing learning as either rote learning

by memorization or meaningful learning by “choosing to relate

new knowledge to relevant concepts and propositions” (Ausu-

bel, 1968: p. 7). Ausubel premise is that the individuals’ prior

knowledge is highly important to their ability to assimilate new

information and learning in a meaningful context to gain a

deeper, most lasting and more complex understanding. He

stated, “the most important single factor influencing learning is

what the learner already knows. Ascertain this and teach him

accordingly” (1968: p. iii). Ausubel’s view backed by construc-

tivism which is a theory had its beginnings with learning ideas

from John Dewey who draw attention to the use by learners of

their individual experiences to make sense of novel information

they were exposed to and also the benefits of social interaction

for the learner. The work of Maria Montessori, Jean Piaget, and

Lev Vygotsky have influenced constructivism as a learning

theory that could be used to develop new cognitive structures

that are more sophisticated and allow learners to increasingly

organize new knowledge (Lambert et al., 2002). From a con-

structivist point of view, prior knowledge influenced what new

ideas learners would be able to grasp and how they would in-

terpret that new information. Therefore a person’s conceptuali-

zation of new knowledge is individual because of the prior

experiences and memories she had, including emotions and

feelings associated with the experiences. Concept mapping,

within this framework, is an effective teaching and learning

strategy to assimilate the previous knowledge with newly in-

troduced concept and therein derive meaningful learning. In the

early 1970s Joseph Novak and his colleagues at Cornell Uni-

versity presented concept mapping as a tool to represent know-

ledge structures. Novak, building on Ausubel’s assimilation

theory, created concept mapping as visual representations of

concepts and the meaningful relationships that exist among or

between related concepts in the form of propositions. Thus, a

concept map is a form of knowledge representation and will

reflect the learner’s knowledge structure in a given topic, sub-

ject, domain or area under discussion and search (Novak, 1977).

The phrase concept maps has been used interchangeably with

mind maps and knowledge maps. However, Cañas and col-

leagues distinguished concept maps from other mapping sys-

tems by their theoretical basis in Ausubel’s assimilation learn-

ing theory and constructivist epistemology, their semi-hierar-

chical organization, the use of unconstrained and meaningful

linking phrases, and the way concepts are defined (Cañas et al.,

2003: p. 13). Given that a concept map is a two-dimensional

K. D. F. ALKAHTANI

diagram that displays relationships between concepts through

using linking words, hierarchically structured with progressive

differentiation, from general to specific. When these relation-

ships are made, learners can draw on what they know and re-

shape it in new and meaningful ways. Therefore, concept map-

ping which “developed specifically to tap into a learner’s cog-

nitive structure” is effective metacognitive mechanism that

increases learner knowledge of a particular topic (Novak &

Gowin, 1984: p. 40). Seeing that the literature stresses the im-

portance of the conceptual mapping, I designed my courses to

introduce students to this method of organizing and forming

information into meaningful learning.

Method

Participants

A total of twenty-nine graduate students who were enrolled

in a core course titled Research Design and Its Application in

Special Education (SPED 520). Fifteen students were enrolled

in the first semester and fourteen were enrolled in the second

semester of the academic year 2011-2012.

Design

Integration of qualitative and quantitative approaches (mix-

ed-methods) in a participatory action research (PAR) with pre

and post-evaluations.

Data Collection

The quantitative data consisted of three parts: 1) students’

results at the Assessment Rubric for Research Proposal (ARRP);

2) students’ scores on Self-Directed Learning Readiness Scale

(SDLRS); and 3) students’ responses to anonymous 29-item

course and faculty evaluation survey that used a 5-point Likert

rating scale (1 = strongly disagree, 5 = strongly agree). The

qualitative data included analyses of students’ concept maps,

and semi-structured individual and focus groups interviews.

Procedure

This action research is as much about process as it is about

product. The process of this study is relatively simple and con-

sisted of the Plan-Act-Observe-Reflect research cycle (Creswell,

2008; Mertler, 2006; Taylor et al., 2008). Students were

co-researchers who actively involved in generating and collect-

ing information (Mordock & Krasny, 2001). Evidence for the

problem was gathered from group discussions and results of

ARRP and SDLRS. Results of the pre-assessment were consid-

ered the baseline data and also used to shape the plan of action.

The plan of action is to promote self-direction in learning by

employing strategies which aid learners in learning how to

learn and think for themselves. I utilized concept mapping

strategy as it is aim is aiding learners during their learning

process. Essential elements of implementing the plan were

weekly reflection, focus groups interviews, constructing con-

cept maps, and tracing the progress of the research proposals of

the student. At the end of the course, final research proposals

were assessed using the ARRP. Students were asked to com-

plete the SDLRS and the SPED 520 course and faculty evalua-

tion survey.

Implementation of my action plan requires learners to be

cognizant of the concept mapping process. Therefore, to ensure

familiarity with concept mapping, all students participating in

this study were introduced to this technique by receiving in-

struction on how to develop a concept map in a two-hours ses-

sion. This training was presented, in the second session of the

course, based on Novak and Gowin’s recommended strategy of

teaching concept mapping construction to learners (Novak &

Gowin, 1984: pp. 32-34; Novak, 1998: p. 227). Training mate-

rial consisted of two sections. The first section included a con-

ceptual framework and illustration of concept mapping tech-

nique and instructions on how to create a concept map. The

second section included an article of the students’ own choice

so the students use it to create a concept map. The article se-

lected by the students to reduce unnecessary cognitive overload

caused by unfamiliar content. This allowed them to focus on

concept mapping practice itself. I, at the end of the training

session, asked the students to generate concept maps for their

research proposals. These maps were used throughout the

course to develop research proposals. Concept maps should be

extensions of the learner’s natural cognitive chunking and link-

ing, therefore I asked my students to revise and reflect on their

maps weekly. I was aware of the fact that with no controls on

the students this would be “soft” data, but I believe that the

personal nature of the concept mapping manifested itself by

establishing consistent differences between learners. I also be-

lieve that comparing students to another on the basis of their

concept maps would not be fruitful as I was not looking for one

pattern among learners but rather for consistent pattern exhib-

ited by an individual.

As the idiosyncratic nature of self-directed learning is pre-

served by allowing learners to pursue their own paths, I wanted

my students to have enough freedom to apply their concept

maps in whatever way they will find helpful and fit their needs.

I informed my students that the strength of their concept map is

what the map meant to them, not what someone else understood

from it. I also advised them not to compare their concept maps

to anyone else’s, but rather use these maps to their advantage

by linking new information, from the course lectures and read-

ing assignments, with their previous knowledge. I asked my

students to hand in a copy of their concept map in progress

before each lecture. By the end of the course, I collected thir-

teen concept maps from each student, the first and last maps

were considered as the pre and post-intervention concept maps.

Credibility

Credibility (validity and reliability) of knowledge acquired

from an action research is immediately recognized as workable

and effective in solving problems or developing practice by

those in similar situations (Somekh, 2006). Enhancing the

credibility of action research can be acquired by using data-

triangulation (Creswell, 2008; Mertler, 2006; Taylor et al., 2008;

Teddlie & Tashakkori, 2009). In this study triangulation of data

sources include quantitative data (students’ scores on ARRP

and SDLRS, students’ responses to course and faculty evalua-

tion survey) and qualitative data (multiple interviews, analyses

of students’ concept maps).

Results and Discussion

Data from both the first and the second semesters were

merged to represent an accurate representation of SPED 520

112

K. D. F. ALKAHTANI

praxis. Quantitative data were analyzed using the Statistical

Package for the Social Science s, SPSS-20. The paired-sample t

test was used to quantify the effect of the concept mapping

strategy on students’ production of viable research proposals as

measured by ARRP. The mean of the pre-intervention scores

were compared with the mean of the post-intervention scores.

The results indicated that the two scores were significantly

different (t = 53.219, p = 0.000) suggesting that students dis-

played significantly higher post-intervention scores at the 0.01

level compared to their pre-intervention scores on ARRP. Ef-

fect sizes was (9.88) by Cohen’s d and (0.99) by effect-size r

for paired data. Both are large effect sizes. Results of the analy-

sis are summarized in Table 1 and Figure 1.

To verify if there was a difference between the pre-interven-

tion and the post-intervention scores on the SDLS the paired-

sample t test was used. The mean of the pre- and post-interven-

tion scores were compared. The results revealed that the two

scores were significantly different (t = 2.626, p = 0.014) which

signify that the students demonstrated significantly higher

post-intervention scores at the 0.05 level compared to their

pre-intervention scores on SDLR. Effect sizes was (0.5) by

Cohen’s d and (0.18) by effect-size r for paired data. Both are

medium effect sizes. Results of the analysis are presented in

Table 2 and Figure 2.

Pearson’s correlation coefficient was conducted to evaluate

the relationship between students’ level of self-direction in

learning and their ability of writing viable research proposals. A

strong positive correlation was found between students’ scores

on the SDLS and their scores in the ARRP, for the pre-inter-

vention’ scores was (r = 0.692, p < 0.0001) and for the post-

intervention’ scores was (r = 0.670, p < 0.0001). This correla-

tion shows a significant relationship at the 0.01 level between

the students’ scores on the SDLS and ARRP in both the pre-

and post-intervention. Table 3 and Figure 3 present this re-

sults.

Of the 29 participants, 27 completed the anonymous stan-

dardized university evaluation survey. This course and faculty

evaluation survey compare faculty performance with the aver-

age of other faculty members in the department, college, and

university. Analysis of the group mean score revealed that

SPED 520 course rating were higher than classes throughout

the department, college, and university. Group means are

shown in Table 4 and Figure 4.

It was not possible to correlate course evaluation scores with

ARRP and SDLRS scores, as the results were obtained without

identification.

Qualitative data were analyzed to provide additional insight

into the quantitative results. Following the individual and focus

groups interviews the qualitative data was coded based on the-

matic analysis. Themes emerged were: 1) self concept as

learner; 2) motivation for learning; and 3) using concept map-

ping for writing.

The first theme, self concept as learner, looks at participants’

perception of themselves as learners. At the beginning of the

course, students indicated that they mainly prefer teacher-cen-

tered learning approach. This was signified by their responses

to the question “How would you describe yourself as a

learner?” One student stated that “I understand that there are so

many innovative ways to learn but I prefer a lecture where the

teacher is the most active person and answers my questions

immediately. I prefer learning this way because my whole life

I’ve been taught to think that way.” Responses from the

post-intervention interviews suggest that concept mapping in-

creased students awareness and reinforced their views of their

personal identity as active learners. One student stated that

“using concept mapping made me more aware of how I can

intentionally make my learning more effective. I value the use

of concept mapping and how it can make such a difference in

my future education”.

The second theme, motivation for learning, concerns about

participants’ perspective of their motivation. The responses

received in pre and post-intervention interviews did differ. The

majority of respondents, at the pre-intervention interviews,

reveal external motivation for learning (e.g., achieving good

grades, social status) as evidenced by their responses to the

question “What motivates you to learn?” One student indicated

that “Having very good grades in classes and a high GPA, I

want to further my studies to doctoral degree. Having a PhD

will increase my career options and raise my social status” Re-

sponses from the post-intervention interviews suggest that stu-

dents become more motivated internally as they implemented

concept mapping in their learning. One student indicated that “I

Table 1.

Comparison betwee n the pre and post-intervention scores on the ARRP.

Variable MeasureNMean SD t-value dfp-value

Pre-test2926.48 7.03

ARRP ScoresPost-test2981.03 8.06 53.219 280.000

100

1234567891011121314151617181920212223242526272829

scor es

Students ARRP‐Pre

ARRP‐Post

Figure 1.

Comparison of participants’ pre- and post-scores on ARRP.

K. D. F. ALKAHTANI

found using concept mapping to be very challenging and re-

warding at the same time. I enjoy reading more and how I put

my opinions across while creating concept maps that really

make reading and learning meaningful”.

The third theme, using concept mapping for writing, is based

on the claim that concept mapping might aid the writing proc-

ess. In response to the question “Did the concept mapping aid

you in writing your proposal?” the students stated that concept

mapping helped them to organize their knowledge and writing.

The following quote is representative of the comments made by

students who stated they benefit from concept mapping: “Map-

ping concepts from different reading materials helped me to

make sense out of literature and to identify the major themes to

be covered in my proposal. I also believe that concept mapping

helped me in studying different opinions and ideas which situ-

ate my research questions and design within the literature I

reviewed”. The least enthusiastic support for using concept map-

ping by the students, who were struggling with the mapping

Table 2.

Comparison betwee n the pre and post-intervention scores on the SDLS.

Variable MeasureNMean SD t-value dfp-value

Pre-test29212.4 13.78

DLS scoresPost-test29213.0 13.58 2.626 280.014

Table 3.

Comparison between the pre and post-intervention scores on the SDLS

and APRP.

Measure VariableNMean SD r-value p-value

ARRP 2926.48 7.033

Pre-scores SDLS 29212.4 13.78 0.692 0.000

ARRP 2981.03 8.059

Post-scores SDLS 29213.3 13.58

0.670

0.000

100

150

200

250

300

1234567891011121314151617181920212223242526272829

Score s

Students DSLS‐Pre

DSLR‐Post

Figure 2.

Comparison of participants’ pre- and post-intervention scores on DSLS.

300

250

200

150

100

01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Students

Scores

DSLR-Post

ARRP-Post

300

250

200

150

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Students

Scores

DSLS-Pre

ARRP-Pre

Figure 3.

Correlation between students’ scores on the SDLS and ARRP in the pre- and post-intervention.

114

K. D. F. ALKAHTANI

procedure, was reflected in the statement “Not really, although

concept mapping helped me to identify concepts they should

link with others, but it was difficult to find the right word to

link the different concepts. Instead of understanding the mate-

rial, I was focusing on finding linking words that made sense to

the concepts. I believe that concept mapping helped me in

finding key ideas for writing my proposal, but I need to have an

expert opinion and support during the writing process of my

proposal”.

Pre and post-intervention concept maps were compared in a

qualitative manner. Evaluation of concept maps was based on

several characteristics including number of nodes, propositions,

cross-links, structure classification, and overall perceptions.

Data from analyzing the post-intervention concept maps indi-

cate that there is an improvement in concept map quality over

time. This result is in line with previous research (e.g., Fisher et

al., 2000; Jacobs-Lawson & Hershey, 2002) showing that the

level of concept map’s complexity enhan c ed with practice.

I further wanted to understand how concept mapping influ-

ence students’ research proposal development and writing.

Therefore, using “soft” data measure classification, I classified

the students in groups based on their performance on the

post-concept map and the ARRP final scores. There were three

categories of students’ performance as follows: 1) students who

created rich maps and produced good proposals; 2) students

who created average maps and produced average proposals; and

3) students who created average maps and produced good pro-

posals. Results of the classifications are presented in Table 5.

The result regarding group C was not predicted, I was ex-

pecting that all the students who created rich maps will also

produced good proposals. I was wrong, there were five students

who performed well above average on the APRP but their maps

contain less information and detail than the maps of other stu-

dents who produced good proposals. One important question

came to my mind: How did my students decide the amount of

information to put in their maps? I, to find an answer to my

question, contacted three students and set up interviews with

them. The three students were not chosen randomly, they were

selected from each group. Students’ responses to my question

were interesting and crystallizing moment for me to understand

their maps. All the three students thought that their maps are

readable and useful as they tried to include all the information

that they think they will need. It is clear that my students did

what I have told them to do. They use their maps as highly

personal learning tools to understand, remember, or summarize

information. The student from group A (S. A.) said that “con-

cept mapping is a useful way to understand by tying together all

the new information with what I had learnt”. The student from

group B (N. A.) used concept mapping to summarize informa-

tion, she state “I primarily use concept mapping to summarize

information that I have gathered and is important for my sub-

ject”. The student from group C, which is the group I was

deeply concerned about, (B. H.) declared that concept mapping

had helped greatly in defining and writing her research proposal.

I placed her map on the table and asked how did this help? She

pointed at the map and said “those links and different layers let

me see the big picture and remember. It’s surprising how much

I forget. I think concept mapping is a good tool for reviewing

and remembering. My map only include the key concepts that I

need to remember the information”. I concluded from the stu-

dents’ comments about their maps that the strength of the con-

cept mapping is not only the amount of information (number of

nodes), but also how it sparks in the mind of the map creator.

There was no student who could created rich map and produced

average proposal which indicate the importance of concept

mapping in the process of creating a concrete research proposal.

Finally, I was pleased to find that 79% of the students had pro-

duced good proposals using concept mapping as a learning

strategy.

Conclusion

In general, the post-intervention assessment data point to-

ward positive changes. All students developed significantly

Table 4.

Mean scores of course and faculty evaluation survey.

Classes Mean score

Classes throughout the University 3.83

Classes throughout the College of Education 3.97

Classes throughout the Departme n t of Special Education 3.91

SPED 520 Cl ass 4.36

3.83 3.97 3.91

4.36

0.5

1.5

2.5

3.5

4.5

UniversityCollegeDepartment SPED520

Figure 4.

Mean scores of course and faculty evaluation survey.

K. D. F. ALKAHTANI

Table 5.

Students’ classifications based on their performance on the post-con-

cept map and the ARRP final scores.

Examples of student perfo rmance

(post-interve nti on s c ore s)

Group No. of

students Student Post-map ARRP Students’ Comments

A 18 S. A. 88 95 Maps were used

to understand information.

B 6 N. A. 63 78 Maps were used

to remember information.

C 5 B. H. 65 96 Maps were use d to

summarize in f o rmation.

more concrete research proposals. They also created more

complex concept maps and their scores on SDLRS were in-

creased. Findings from analyzing qualitative data suggest that

using concept mapping assist and motivate students to generate

more concepts and assimilate the previous knowledge with

newly generated concepts. This conclusion backed by Novak

and Canas argument that “while at first glance concept maps

may appear to be just another graphic representation of infor-

mation, understanding the foundations for this tool and its

proper use will lead the user to see that this is truly a profound

and powerful tool” (2006: p. 31). I also believe that using con-

cept mapping develop tendencies for self-directed learning

among my students. However, results should be interpreted

with caution due to limitations pertaining to the design and

participants. Further research should be conducted to imple-

ment concept mapping in higher education as I believe this

approach aid learners and teachers in reshaping their knowledge

in progress and meaningful ways. Finally, not only the results,

but the whole experience I had during this study as a teacher-

action researcher has taught me to value meaningful education

more.

Acknowledgements

I would like to thank the Deanship of Scientific Research at

King Saud University for supporting this research financially.

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