Exploring students' cognitive structures in learning science: a review of relevant methods

This article was downloaded by: [National Chiao Tung University 國立交通大學]

On: 27 April 2014, At: 21:15

Publisher: Routledge

Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer

House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Biological Education

Publication details, including instructions for authors and subscription information:

http://www.tandfonline.com/loi/rjbe20

Exploring students' cognitive structures in learning

science: a review of relevant methods

Chin-Chung Tsai

& Chao-Ming Huang

a

Institute of Education and Center for Teacher Education, National Chiao Tung

University , Hsinchu, Taiwan

b

Department of Earth Sciences, National Taiwan Normal University , Taipei, Taiwan

Published online: 13 Dec 2010.

To cite this article: Chin-Chung Tsai & Chao-Ming Huang (2002) Exploring students' cognitive structures

in learning science: a review of relevant methods, Journal of Biological Education, 36:4, 163-169, DOI:

10.1080/00219266.2002.9655827

To link to this article:

http://dx.doi.org/10.1080/00219266.2002.9655827

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained

in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no

representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose

of the Content. Any opinions and views expressed in this publication are the opinions and views of the

authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not

be relied upon and should be independently verified with primary sources of information. Taylor and Francis

shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and

other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation

to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematic

reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any

form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

http://

www.tandfonline.com/page/terms-and-conditions

flev/ew

Exploring students' cognitive

structures in learning science:

a review of relevant methods

Chin-Chung Tsai

and Chao-Ming Huang

Institute of Education and Center for Teacher Education, National Chiao Tung

University, Hsinchu, Taiwan

; Department of Earth Sciences, National Taiwan

Normal University, Taipei, Taiwan

Understanding how people think and how people organise knowledge are always major concerns for educa­

tional researchers. Hence, educators have developed various ways of representing learners' 'cognitive struc­

tures'. This article provides a review of the use of five methods of representing cognitive structures - free word

association, controlled word association, tree construction, concept map and flow map. Through comparing the

types of analyses that are generated from these cognitive structure representation methods, this paper dis­

cusses the applications, as well as the limitations, among these methods.

Key words: Cognitive structure, Constructivism, Assessment, Concept map, Flow map.

Introduction

In the paradigm of constructivism, knowledge is actively con­

structed and personally situated. Hence, every individual may

have different ways of organising knowledge. Exploring every

individual's so-called 'cognitive structure' may become highly

important when studying students' learning. Moreover, current

practice in constructivism advocates the use of multiple methods

of assessment for enhancing learning outcomes (Tsai, 1998a,

2000a, 2001a). Educators may include cognitive structure

assessment as one of the multiple assessment modes (Shavelson

et ai, 1990). In addition, an appropriate reflection and

self-assessment of individual learning processes will facilitate

conceptual change and development (Baird et ai, 1991).

Through explicit analyses of the learner's cognitive structures,

educators can not only understand the student's alternative

conceptions (or misconceptions), but also help the student

engage in metacognitive learning and thus enhance his or her

learning outcomes.

Eylon and Linn (1988) have devised four categories of

research in science learning:

• conceptual learning

• development

• differential

• problem solving

Conceptual learning focuses on the qualitative differences of

concepts or the content and structure of knowledge students

have acquired. Research about student 'misconceptions' or

'alternative conceptions' in recent decades is classified as this

category. Misconceptions are those held by students that are at

variance with scientific knowledge even after formal instruction

(Yip, 1998). Development studies how students attain under­

standings throughout their lives. For example, students may

develop different abilities in various life stages. A growing

interest in this aspect may result in more attention to infor­

mation processing capacity in relation to developmental stages

during maturation. The category differential places an emphasis

on individual differences in ability and aptitude. Moreover,

research in this area probes the interaction of these differences

with instruction. Problem solving explores the processes students

employ to respond to scientific questions and open-ended,

inquiry learning environments. In particular, problem solving

research typically has investigated the differences in solving

strategies between experts (e.g. scientists) and novices (e.g.

students).

These four categories have some interesting relationships

with findings from research on learners' cognitive structures. For

example, it is known from problem solving research that experts

can store and retrieve more information bits than novices.

Former studies have also revealed that experts can store their

information much more efficiently and retrieve it much faster

than novices. This hypothetically occurs due to the

well-elaborated cognitive structures of the experts (Chi et ai, 1985;

de Jong & Ferguson-Hessler, 1986; Larkin et al., 1980). That is,

experts have well-developed or more integrated knowledge

structures to help them solve problems. Furthermore, through

exploration of cognitive structures, educators can better under­

stand students' conceptual development in science and have

identified their alternative conceptions or other non-scientific

ways of explaining phenomena. This has had a practical benefit

of improving science curricula and learning activities that take

prior knowledge structures into account, and incorporate ways

of helping students to meaningfully reorganise their under­

standings, to arrive at a more scientifically accurate view of

Q

ural phenomena. Educators can also explore how students of

different abilities and aptitudes, and instruction with varied

approaches, may have impacts on students' cognitive structures.

If researchers have better ways of analysing students' cognitive

structures, it is very likely that we can improve research on

these four aspects of science learning.

In summary, having evidence of a learner's cognitive struc­

ture could be a fundamental step when we are looking toward

understanding how students construct knowledge, which could

be used to build up further knowledge during subsequent

learning. In this article, we will discuss the significance of

exploring cognitive structure and then review some methods

and their forms of representation with a critical comparative

analysis of their advantages. Some suggestions toward employ­

ing these methods in further research in science education will

also be proposed.

The significance of exploring cognitive

structure

A cognitive structure is a hypothetical construct representing the

relationships of concepts in a learner's long-term memory

(Shavelson, 1974). Some researchers may use different terms to

describe cognitive structure, such as, for example, structural

knowledge (Jonassen et ai, 1993; Diekhoff & Diekhoff 1982). As

they ascertain, structural knowledge shows the interrelationships

between ideas in a knowledge domain. Besides, structural know­

ledge is related to information processing for organised networks

of ideas stored in semantic or long-term memory. In summary, a

cognitive structure contains learners' existing experiences and

knowledge that will dominate their reconstruction and infor­

mation processing of the incoming stimuli (Tsai, 2001b).

Educational research has repeatedly revealed that many stu­

dents put great effort in memorising, but only a few can apply

disciplinary knowledge to their daily life or decision making

(Tsai, 1998b, 1998c). Many students' cognitive structures are a

collection of isolated bits of information. It is plausible that poor

cognitive structure will result in poor information processing or

inefficient acquisition of new knowledge, and ultimately this

will influence one's academic achievement and ability to apply

knowledge to daily situations.

To state it more specifically, what benefits can educators

obtain from exploring one's cognitive structure? We can briefly

answer this question from three perspectives, including prior

knowledge, assessment and metacognition. By means of explicit

representation of learners' cognitive structures elicited before

instruction, educators can obtain their prior knowledge (Ausubel

et ai, 1978) or alternative conceptions (Wandersee et al, 1994).

Even though there is a plethora of terms concerned with

so-called prior knowledge in science (e.g. intuitive science - Preece,

1984; naive theory - White & Gunstone, 1989), educators still

have some convergent views about prior knowledge. Prior

knowledge at least has the following four attributes:

• It is mainly based on students' life experience;

• Students' prior knowledge sometimes is different from for­

mal knowledge used by scientists or teachers;

• It is resistant to change, or it is tenacious, even through con­

ventional formal instruction;

• Prior knowledge will influence learning processes or con­

ceptual development.

The exploration of cognitive structure can help teachers

know what their students have already assembled in memory

and to what extent it is compatible with accepted norms in a

disciplinary field or incongruent. Knowing one's prior knowl­

edge can guide teachers to design appropriate teaching strate­

gies, assist students to connect past experiences and new

incoming information, and consequently enhance meaningful

learning. Therefore, knowing a student's alternative conceptions

can not only help teachers improve teaching strategies but also

help students work on conceptual change (Posner et ai, 1982).

Current practice in education encourages the use of multiple

ways to assess students' performance. Here, we propose that the

measurement of cognitive structure can be one of the better

indicators in assessing what learners know rather than tradi­

tional paper-and-pencil tests. In other words, through probing

students' cognitive structures, educators can understand what

students learn and how their knowledge may change during the

learning processes. The assessment of students' cognitive struc­

tures can partially replace the traditional paper-and-pencil test.

As Shavelson etal. (1990) mentioned, assessment has to provide

two valid indicators - cognitive fidelity and process relevance.

Cognitive fidelity indicates the congruence of conceptual under­

standing. Since it concerns the major organising principles that

guide knowledge construction, it will influence students' atten­

tion, judgement, plans and goal for learning. Process relevance

assesses how well students apply learned concepts or skills to

their daily life. The assessment of cognitive structure may offer

more information about these two indicators than traditional

paper-and-pencil tests. For instance, through analysing student

concepts and their relationships shown in cognitive structures,

the indicator of cognitive fidelity can be revealed. Also, stu­

dents' cognitive structures may more possibly display how they

relate learned concepts to life experiences, as cognitive struc­

tures can allow students more flexibility in expressing their

ideas. Investigating students' cognitive structures is not only a

practical assessment method, but also offers the opportunity for

teachers to examine their teaching strategies. By monitoring stu­

dents' cognitive structure development combined with the

teacher's self-reflection of his or her own teaching strategies, the

teacher can lead to more relevant and informed design of learn­

ing experiences that complement the cognitive development of

the learners.

As a result, numerous biology educators and teachers have

tried to use the cognitive structure exploration (such as the

methods of concept mapping or word association) in practice.

For example, Marbach-Ad (2001) used concept maps (and

some interviews) to probe the comprehension of genetic con­

cepts among a group of ninth graders (14- and 15-year-olds),

12th graders (17- and 18-year-olds) and trainee teachers, and

found that genetic instruction in ninth and 12th grade and in

college needed improvement. Bahar et al. (1999) used word

association tests to explore 280 first-year biology (college) stu­

dents' cognitive structures about genetics. The words such as

'gene', 'cell division' and 'chromosome' were provided to act as

stimuli for the tests. The study revealed that the students gen­

erated many ideas related to given key words, but they did not

see the overall picture as a network of relevant ideas. Tsai and

Huang (2001) documented a group of fifth graders'

(11-year-olds) cognitive structure development about the topic of bio­

logical reproduction, and found that the rich connections

(

j

Students' cognitive structures

Tsai and Huang

between c o n c e p t s and higher-order cognitive o p e r a t i o n s m i g h t facilitate t h e m a t u r a t i o n of c o n n e c t e d k n o w l e d g e in m e m o r y . They suggested t h a t biology t e a c h e r s n e e d e d t o e n c o u r a g e stu­ dents to use higher level i n f o r m a t i o n processing d u r i n g biol­ ogy instruction.

Finally, an analysis of one's o w n cognitive s t r u c t u r e s , for e x a m p l e w h e n a s t u d e n t is allowed t o e x a m i n e a m a p or o t h e r representation of h e r or his k n o w l e d g e recall, can e n h a n c e m e t a c o g n i t i o n and m o r e reflective analysis of h o w t o i m p r o v e one's o w n learning (Novak, 1990; Tsai, 2 0 0 1 b ] . T h e s t u d e n t can retrospectively reflect on his or h e r specific c o n c e p t s or alternative c o n c e p t i o n s and c o m p a r e existing s t r u c t u r e s in m e m o r y w i t h previous ones at an earlier t i m e , or in relation t o t h e k n o w l e d g e organisation of others. S u c h reflection can facilitate individual critical reflection d u r i n g learning h o w t o learn, and m o r e o v e r e n h a n c e c o n c e p t u a l d e v e l o p m e n t (Baird et ai, 1991). In addition, p r e s e n t i n g and discussing s t u d e n t s ' cognitive s t r u c t u r e s can allow s t u d e n t s t o scrutinise t h e i r thinking (Diekhoff & D i e k h o f f 1 9 8 2 ) , and t h e n p r o m o t e higher order learning o u t c o m e s .

In conclusion, revealing s t u d e n t s ' cognitive structures can have benefits to b o t h t h e teacher, in designing learning, and to t h e learner in enhancing skills t h a t p r o m o t e m o r e self-directed learning. For teachers, revealing s t u d e n t s ' cognitive s t r u c t u r e can assist teachers to p r o b e s t u d e n t s ' prior knowledge and t h e n develop m o r e appropriate instructional strategies to e n h a n c e learning outcomes. O n t h e o t h e r hand, probing s t u d e n t s ' cogni­ tive structures can also help teachers t o assess w h a t s t u d e n t s have learned during t h e teaching processes. As a metacognitive tool, revealing cognitive structures can facilitate c o n c e p t u a l d e v e l o p m e n t and conceptual change.

A

review of different methods

In recent decades, researchers have p r o p o s e d several ways of representing people's cognitive structures. Major issues in representing people's cognitive structures include h o w t o use quantitative t e r m s for a valid description and h o w to display t h e cognitive structure information t h r o u g h visual formats.

T h e r e are five c o m m o n m e t h o d s of eliciting and representing cognitive structures - free w o r d association, controlled w o r d association, tree construction, c o n c e p t m a p and flow m a p . T h e following is a brief overview of t h e p r o c e d u r e s typically used for these five m e t h o d s :

1. Free word association

T h e administrators or experts offer a m a i n c o n c e p t and ask a respondent to write d o w n all relevant concepts and as m a n y as possible on a plain paper w i t h o u t t i m e limit. In this m e t h o d , t h e r e s p o n d e n t is asked t o repeat t h e s a m e w o r d association task after completing t h e previous test, a total of 10 times. Figure 1 shows a sample of a free w o r d association in w h i c h t h e m a i n concept is photosynthesis. In this figure, t h e r e s p o n d e n t is asked t o write d o w n related concepts freely. After c o m p l e t i n g t h e w h o l e test, researchers c o u n t t h e frequency of r e s p o n d e n t -recalled concepts from t h e ten free w o r d association sheets. According to t h e semantic similarity, researchers d e t e r m i n e t h e frequencies of a pair of concepts and t h e n use a m a t r i x to cal­ culate their distance values and to display t h e connections among concepts.

Photosynthesis

2. Controlled word association

The test administrators offer a m a i n c o n c e p t and limited space for a r e s p o n d e n t to w r i t e d o w n relevant concepts. This m e a n s t h a t t h e r e s p o n d e n t s have t o decide w h i c h c o n c e p t is a very i m p o r t a n t o n e t h a t is related t o t h e main c o n c e p t . This eliciting process has t o b e c o m p l e t e d w i t h i n o n e m i n u t e per m a i n con­ cept. Figure 2 shows a sample of controlled w o r d association w i t h t h e s a m e m a i n c o n c e p t of photosynthesis. Clearly, t h e m o s t i m p o r t a n t difference b e t w e e n t h e free w o r d association and controlled w o r d association is their scoring m e t h o d . In t h e controlled w o r d association, t h e r e s p o n d e n t has t o take t h e con­ c e p t s ' i m p o r t a n c e into a c c o u n t and arrange their o r d e r a m o n g elicited c o n c e p t s in accordance w i t h his/her cognitive struc­ tures. C o n t r o l l e d w o r d association also n e e d s c o m p l e x m a t r i x calculation (for details, see Jonassen et al, 1993).

Rank

Photosynthesis

Sun light Carbon dioxide

chlorophyl energy Figure 2 Controlled word association.

3. Tree construction

Tree construction is t h e a r c h e t y p e of c o n c e p t m a p p i n g . In t h e t r e e construction m e t h o d , t h e r e s p o n d e n t will w r i t e d o w n a rel­ evant c o n c e p t t h a t has s o m e relation w i t h t h e former o n e from a given c o n c e p t list. This t e c h n i q u e can s h o w h o w close t h e relationship is a m o n g pairs of c o n c e p t s and also their hierarchi­ cal attributes as o b t a i n e d w i t h a c o n c e p t map. Figure 3 shows a sample of a cognitive s t r u c t u r e t h a t is derived from a tree con­ struction. To portray t h e interrelationships of t h e ideas obtained from t h e t r e e construction, a c o m p l e x m a t r i x calculation is also necessary (for details see Jonassen et ai, 1993).

Sunlight

Carbon dioxide 4 Oxygen

Figure 3 Tree construction.

4. Concept map

T h e unit of a c o n c e p t m a p is a proposition. Two concepts and one linkage constitute one proposition. A c o n c e p t m a p n o t only shows t h e relationships b e t w e e n concepts, b u t also displays t h e

) Students' cognitive structures Tsai and Huang

hierarchy of cognitive structures, when the student properly fol­

lows the rule of subsumptive organisation of ideas while con­

structing the maps. Figure 4 shows a sample of a concept map

constructed by a fifth grader (11-year-old). Concept map con­

struction requires students to integrate and present their con­

cepts hierarchically; therefore the concept map could be a

decorated or elaborated one. This may not show the respondent's

authentic cognitive structure, since there is some constraint

imposed by the administrator in recommending at least that the

information should be organised from most inclusive to least

inclusive. Concept maps have been used more for instructional

than assessment purposes (Ruiz-Primo & Shavelson, 1996).

Figure 4 A concept map about photosynthesis by a fifth grader.

5. Flow map

The flow map method is a relatively new method of represent­

ing learners' cognitive structures. This method will therefore be

described in more detail. The goal of employing a flow map

method is to capture both the sequential and network features

of people's thought in a non-directive way. Interviews are used

to obtain a record of student narratives to be analysed as evi­

dence of the student's cognitive structures. Since this part of the

interview needs to be conducted in a non-directive way to help

the student express what he or she knows with minimum bias

by the interviewer, the interview questions are kept as simple as

possible. For example, in the case of photosynthesis, the inter­

view questions could be:

(1) Please tell me what the main parts of the photosynthesis

process are.

(2) Can you tell me more about the parts you have identified?

(3) Can you tell me the relationships among some of the

ideas you have already told me?

The responses to these questions are tape-recorded, and then

transcribed into the format of a flow map. Figure 5 demon­

strates a sample flow map that came from the same respondent

in Figure 4. Basically, the flow map is constructed by entering

the statements (equivalent to a clause or sentence) in sequence

as they were mentioned by the student. The sequence of dis­

course is examined and recurrent ideas represented by recurring

word elements in each statement (representing a connecting

node to prior thought) are linked by connecting arrows. The lin­

ear or serial arrows show the direct flow of student narrative,

while recurrent linkages show revisited ideas among the state­

ments displayed in the flow map. For example, the student's

narrative mapped in Figure 5 shows a sequential pattern begin­

ning with the conditions of photosynthesis and then its products

and functions. Moreover, recurrent arrows are inserted that link

revisited ideas to the earliest step where the related idea (i.e.

revisited idea) first occurred. Statement five, for example, 'The

oxygen produced can help people breathe' includes one major

revisited idea 'oxygen'. Therefore, statement five has one recur­

rent arrow drawn back to statement four (i.e. the earliest step

containing a statement about oxygen; for further details about

the flow map method, see Anderson & Demetrius, 1993;

Anderson, Randle & Covotsos, 2001; Bischoff & Anderson,

1998, 2001; Tsai, 1998b, 2000b, 2001b; Tsai & Huang, 2001).

A flow map representation has a merit of exhibiting both the

sequential pattern of recall and also evidence of an underlying

interconnected texture of ideas in cognitive structures.

Researchers can also estimate the respondent's information

retrieval rate by entering time markers on the flow map at reg­

ular intervals as the narrative unfolds (shown in Figure 5). It

should be noted that the time is measured after subtracting the

interval of time where the interviewer is speaking. That is, only

the time elapsed during the respondent's narrative is included.

>

>

- ►

- * ■

1 .Plants with chlorophyll can work on

photosynthesis. (8 seconds)

2.Most plants have chlorophyll. (15seconds)

ir

3.Photosynthesis also needs sunlight. (27

seconds)

1 f

4.Photosynthesis can consume carbon

dioxide and produce oxygen. (45 seconds)

i

r

5.The oxygen produced can help people

breathe. (62 seconds)

\ '

6.We will die, if we cannot breathe. (71

seconds)

1

r

7.Hence, it is necessary for us to have more

Figure 5 A flow map about photosynthesis by a fifth grader.

A comparison of methods

There are three major aspects in describing cognitive structures,

including the concepts or ideas contained, the connections

among concepts and the information processing skills. Among

the criterial variables used to evaluate elicited concepts are their

Q

concepts within an individual's cognitive structure, while t h e cor­ rectness deals with t h e accuracy among concepts expressed by t h e respondent. In addition to t h e extent and correctness, t h e con­ nection among concepts in cognitive structures is another impor­ tant issue. Connection indicates t h e degree of integration among concepts. Availability - h o w facile t h e respondent is in retrieving information within a given task context - together with analyses of information processing strategies revealed during information recall can b e used to gain data about information processing skills. Some major aspects and variables of cognitive structure relevant to the t h e m e of this paper are listed in Table 1.

Table 1 Aspects and variables of cognitive structures. Aspects

Concepts Connection

Information processing skills

Variables

Extent, Correctness Integration

Availability, Analyses of information processing strategies

As a result, these t h r e e aspects include five different vari­ ables, w h i c h are described as follows:

1. Extent - t h e n u m b e r of ideas c o n t a i n e d in one's cognitive structures.

2. Correctness - t h e n u m b e r of alternative conceptions shown in cognitive structures, or t h e n u m b e r of correct conceptions. 3. Integration - t h e connection of cognitive structures. A

well-organised s t r u c t u r e is similar t o a well-structured database. Users can find t h e information efficiently. 4. Availability - t h e availability of cognitive structures can b e

represented by information retrieval rate. T h a t is, t h e t i m e required to mobilise and retrieve ideas can s h o w t h e avail­ ability in cognitive structures (Tsai, 1998b, 2 0 0 1 b ) . 5. Analyses of information processing strategies - by m e a n s

of c o n t e n t analyses of cognitive structures, researchers can investigate individuals' information processing operations, such as t h e use of defining, describing, inferring or explain­ ing (Tsai, 1999). For example, t h e first s t a t e m e n t recalled by t h e s t u d e n t in Figure 5 t h a t 'Plants w i t h chlorophyll can w o r k on photosynthesis' can b e categorised as t h e use of 'describing' information processing operation. T h r o u g h categorising elicited c o n c e p t s and t h e i r relationships, researchers can explore t h e information processing strate­ gies among respondents.

Table 2 shows s o m e differences a m o n g five m e t h o d s of r e p ­ resenting cognitive structure in relation to these five variables.

Table 2 The variables of cognitive structures and the methods of probing cognitive structures. Free word association Controlled word association Tree construction Extent Correctness Integration Availability Information processing strategy analyses ** = Definitely, * = Possibly

According t o Table 2, it seems t h a t t h e m e t h o d s of c o n c e p t m a p and flow m a p can offer relatively m o r e information in analysing t h e variables a b o u t cognitive s t r u c t u r e t h a n t h e o t h e r three. T h e information elicited from free w o r d association, controlled w o r d association and tree construction m a y n o t clearly s h o w t h e cor­ rectness of s t u d e n t cognitive structures. For e x a m p l e , if a stu­ d e n t writes 'oxygen' as a relevant c o n c e p t t o ' p h o t o s y n t h e s i s ' in t h e free w o r d association task, researchers still can n o t judge if h e or she has correct k n o w l e d g e c o n n e c t i o n b e t w e e n t h e s e t w o concepts. T h e m e t h o d s of free w o r d association, controlled w o r d association and tree construction also r e q u i r e c o m p l e x m a t h e m a t i c a l calculation w h e n showing t h e integration a m o n g concepts. Besides, repeatedly doing t h e same w o r d association tasks m a y lead t h e r e s p o n d e n t t o b e c o m e b o r e d and this will endanger t h e validity of t h e k n o w l e d g e s t r u c t u r e task.

T h e r e is an i m p o r t a n t difference b e t w e e n c o n c e p t m a p s and flow m a p s t h a t should b e emphasised, t h a t is, 'availability'. In this context, t h e availability of cognitive structures is r e p r e ­ sented by t h e information retrieval rate. T h e c o n c e p t m a p task often asks s t u d e n t s t o c o n s t r u c t as m a n y of t h e i r o w n c o n c e p t s as possible, p e r h a p s w i t h i n fixed interval. Educators can only obtain t h e final result of t h e c o n c e p t m a p and they m a y not catch t h e t i m e c o n s u m e d for m e n t a l information processing by t h e learners. By m e a n s of flow m a p m e t h o d s , researchers can obtain a coefficient of availability by taking t h e q u o t i e n t of t h e n u m b e r of u t t e r a n c e s recalled divided by t h e total time. This is also a form of retrieval index. By c o m p a r i n g retrieval rates a m o n g individuals, especially in relation to differing d e m a n d s i m p o s e d by varying cognitive tasks, researchers can acquire m o r e information a b o u t t h e d y n a m i c n a t u r e of cognitive struc­ t u r e d e v e l o p m e n t (Anderson & D e m e t r i u s , 1 9 9 3 ; Tsai, 1 9 9 8 b ) .

T h e analysis of information processing strategies is a n o t h e r i m p o r t a n t difference a m o n g t h e s e five m e t h o d s . Exploring one's information processing strategies m a y h e l p researchers m o n i t o r a learner's progress during cognitive s t r u c t u r e d e v e l o p m e n t or trace t h e processes of learning as t h e y unfold in a specified p e r i o d of time. W h e n e d u c a t o r s have a tool to analyse t h e d y n a m i c processes and variety of cognitive operations t h a t occur during k n o w l e d g e d e v e l o p m e n t , t h e y m a y have m o r e p o t e n t diagnostic information t o solve p r o b l e m s of s t u d e n t s ' learning and t o offer effective teaching strategies t o p r o m o t e m o r e effi­ cient learning o u t c o m e s . It appears t h a t a m o n g t h e analytical p r o c e d u r e s reviewed here, only t h e flow m a p m e t h o d attains these p u r p o s e s well.

By comparing Figure 4 with Figure 5, which w e r e b o t h obtained from t h e same respondent, it seems that t h e respondent was able t o express m o r e concepts t h r o u g h t h e m e t h o d of flow m a p analysis c o m p a r e d to concept m a p production. It may not be very easy for elementary school stu­ dents t o integrate their ideas or daily experiences into a hierarchical and

Concept map blow map r

integrated framework of concepts, as T* M is r e q u i r e d by a c o n c e p t m a p •» « m e t h o d . Thus, narrative analysis as ** ** used in t h e flow m a p t e c h n i q u e may

b e m o r e effective in tapping t h e e x t e n t and organisation of knowledge », among respondents w h o have limited lexical capacity. Researchers have to take t h e age of t h e respondent into

I j Students' cognitive structures Tsai and Huang

consideration w h e n exploring a learner's cognitive structure. T h e use of flow m a p s m a y gain s o m e s u p p o r t from n e u r o b i -ology research. Recently, evidence from neurobi-ology has b e e n mobilised t o e n h a n c e t h e o r y building and t o provide a scientific framework for u n d e r s t a n d i n g t h e manifold functions of cogni­ tive s t r u c t u r e and processing of information flow in d i e h u m a n brain (Anderson, 1997; Baddeley, 1992; Wilson et al, 1993). This e m e r g e n t field, combining data from t h e neurosciences and cognitive psychology, is generally k n o w n as neurocognitive sci­ ence and has b e g u n t o explain k n o w l e d g e structures, m e t a c o g -nitive processes and p r o b l e m solving, and t h e role of e m o t i o n and motivation in learning in t e r m s of f u n d a m e n t a l neural struc­ t u r e s and their plasticity (Anderson, 1 9 9 1 , 1992). Knowledge structures m a y b e correlated w i t h neural s c h e m a or n e t w o r k s of c o n n e c t e d n e u r o n a l elements, b u t p r o b a b l y of a d i s t r i b u t e d nature. T h a t is, a u n i t of k n o w l e d g e is n o t localisable t o a par­ ticular p o i n t in t h e cerebral cortex b u t is widely e n c o d e d w i t h i n elaborate, e x t e n d e d neural assemblages. Moreover, m e m o r y or t h e recall of e x p e r i e n c e is almost certainly n o t a process of encoding and a part-for-part r e a d o u t as from a t a p e recording. T h e best evidence is t h a t m e m o r y is r e c o n s t r u c t e d from c o m ­ p o n e n t parts of knowledge, assembled in relation t o certain organising principles characteristic of t h e learner's history, and t h e d e m a n d s of t h e task at t h e t i m e of recall. T h u s , k n o w l e d g e structures are viewed as organised b u t also malleable and adapt­ able d e p e n d i n g on t h e stream of ongoing experience. H e n c e , research strategies for r e p r e s e n t a t i o n of k n o w l e d g e s t r u c t u r e s should take into consideration t h e d y n a m i c qualities of recon­ structive m e m o r y in addition t o t h e m o r e stable organising prin­ ciples t h a t give it c o h e r e n c e and a degree of predictability. In light of c u r r e n t neuroscience perspective, t h e flow m a p m e t h o d may b e a b e t t e r way of representing cognitive s t r u c t u r e s a m o n g t h o s e reviewed in this paper, as it can c a p t u r e m o r e a b o u t t h e d y n a m i c n a t u r e of k n o w l e d g e s t r u c t u r e w i t h o u t imposing pre­ d e t e r m i n e d organisation such as hierarchical.

Conclusion

Each of t h e five m e t h o d s reviewed in this p a p e r has its o w n advantages and limitations. For example, free w o r d association, controlled w o r d association and tree c o n s t r u c t i o n r e q u i r e researchers t o c o n d u c t m o r e c o m p l e x m a t r i x calculations t h a n t h e remaining m e t h o d s . T h e calculations are sufficiently c o m ­ plex t h a t sophisticated c o m p u t e r software is required. T h e m o s t powerful m e r i t of c o n c e p t m a p s is t h a t researchers can obtain visual displays of a s t u d e n t ' s cognitive structures generated directly by t h e s t u d e n t w h o draws t h e m a p s . H o w e v e r , researchers m a y need to s p e n d s o m e considerable t i m e on train­ ing s t u d e n t s t o d r a w their o w n c o n c e p t maps, and in t h e process can bias t h e p r o d u c t p r o d u c e d by t h e r e s p o n d e n t . W h i l e flow m a p s can offer richer and s o m e w h a t m o r e powerful indicators for representing cognitive structure, t h e m e t h o d also has its o w n limitations. T h e raw data of flow m a p s are o b t a i n e d from r e s p o n d e n t s ' interview narratives, and t h u s while less p r o n e t o administrative bias, t h e task of transcribing and coding t h e flow m a p can b e t i m e consuming. T h e flow m a p t e c h n i q u e does n o t require extensive t i m e t o train s t u d e n t s in advance; however, researchers have t o d r a w t h e s e flow m a p s individual by individ­ ual. This drawing processing may b e a t i m e - d e m a n d i n g task.

In conclusion, by probing s t u d e n t s ' cognitive structures, teachers can design m o r e a p p r o p r i a t e teaching strategies t h a t

will e n h a n c e s t u d e n t s ' learning o u t c o m e s . Furthermore, t h e t e a c h e r can assess s t u d e n t s ' learning o u t c o m e s t h r o u g h t h e exploration of their cognitive structures. Exposition of students' cognitive structures for their o w n reflective analysis, can also h e l p t h e m u n d e r s t a n d t h e weakness of their knowledge struc­ t u r e s and t o revise their understandings t h r o u g h additional m o r e organised k n o w l e d g e acquisition. A careful reflection and retrospection a b o u t one's o w n k n o w l e d g e structure, or an ana­ lytical c o m p a r i s o n w i t h o t h e r s ' cognitive structures, can help learners construct m o r e integrated and scientifically sound k n o w l e d g e structures for further applications. T h e m e t h o d s reviewed in this p a p e r m a y provide s o m e directions for teachers w h o are interested in exploring s t u d e n t cognitive structures and possibly adapting t h e m e t h o d s to improve their professional practice, or t o begin a classroom-based research program.

Acknowledgements

This research w o r k was s u p p o r t e d , in part, by funds from National Science Council, Taiwan, u n d e r grants N S C 8 9 2 5 1 1 -S-009-027, N S C 9 0 - 2 5 1 l - S - 0 0 9 - 0 0 1 . T h e authors also express their gratitude t o Professor O Roger A n d e r s o n at Teachers College, C o l u m b i a University, for his helpful c o m m e n t s on an early version of this paper.

References

Anderson O R (1991) Neurocognitive models of information processing and knowledge acquisition. Progress in Sensory Physiology, 12,

1 1 5 - 192.

Anderson O R (1992) Some interrelationships between constructivist models of learning and current neurobiological theory, with implica­ tions for science education. Journal of Research in Science Teaching, 29,

1 0 3 7 - 1 0 5 8 .

Anderson O R (1997) A neurocognitive perspective on current learning theory and science instructional strategies. Science Education, 81, 67 - 89.

Anderson O R & Demetrius O J (1993) A flow-map method of repre­ senting cognitive structure based on respondents' narrative using sci­ ence content. Journal of Research in Science Teaching, 30, 953 - 969. Anderson O R, Randle D & Covotsos T (2001) The role of ideational

networks in laboratory inquiry learning and knowledge of evolution among seventh grade students. Science Education, 85, 410 - 425. Ausubel D P, Novak J D & Hanesian H (1978) Educational Psychology:

A cognitive view. New York, USA: Holt, Rinehart & Winston. Baddeley A (1992) Working memory. Science, 235, 556 - 559. Bahar M, Johnstone A H & Sutcliffe R G (1999) Investigation of stu­

dents' cognitive structure in elementary genetics through word asso­ ciation tests. Journal of Biological Education, 33, 134 - 141. Baird J R, Fensham P J, Gunstone R F & White R F (1991) The impor­

tance of reflection in improving science teaching and learning. Journal of Research in Science Teaching, 28, 163 - 182.

Bischoff P J & Anderson O R (1998) A case study analysis of the devel­ opment of knowledge schema, ideational networks and higher cogni­ tive operations among high school students who studied ecology. School Science and Mathematics, 98, 228 - 2 3 7 .

Bischoff P J & Anderson O R (2001) Development of knowledge frame­ works and higher order cognitive operations among secondary school students who studied a unit on ecology. Journal of Biological Education, 35, 81 - 88.

Chi M T H, Feltovich P J & Glaser R (1985) Categorization and repre­ sentation of physics problem by expert and novices. Cognitive Science, 5, 121 - 152.

De Jong T & Ferguson-Hessler M G M (1986) Cognitive structures of good and poor novice problem solvers in physics. Journal of Educational Psychology, 78, 279 - 288.

Diekhoff G M & Diekhoff K B (1982) Cognitive maps as a tool in com­ municating structural knowledge. Educational Technology, 22, 28 - 30. Eylon B & Linn M C (1988) Learning and instruction: An examination

Q

Educational Research, 58, 251 - 301.

Jonassen D H, Beissner K & Yacci M (1993) Structural knowledge: Techniques for representing, conveying and acquiring structural knowl­ edge. New Jersey, USA: Lawrence Erlbaum Associates, Inc.

Larkin J, McDermott J, Simon D P & Simon H A (1980) Expert and novice performance in solving physics problems. Science, 208, 1 3 3 5 - 1342.

Marbach-Ad G (2001) Attempting to break the code in student com­ prehension of genetic concepts. Journal of Biological Education, 35, 1 8 3 - 189.

Novak J D (1990) Concept mapping: A useful tool for science educa­ tion. Journal of Research in Science Teaching, 27, 937 - 949.

Posner G J, Strike K A, Hewson P W & Gertzog W A (1982) Accommodation of a scientific conception: Toward a theory of con­ ceptual change. Science Education, 66, 211 - 227.

Preece P F W (1984) Intuitive science: Learned or triggered? European Journal of Science Education, 6, 7 - 10.

Ruiz-Primo M A & Shavelson R J (1996) Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569 - 600.

Shavelson R J (1974) Methods for examining representations of a sub­ ject-matter structure in a student's memory. Journal of Research in Science Teaching, 11, 231 - 249.

Shavelson R J, Carey N B & Webb N M (1990) Indicators of science achievement: options for a powerful policy instrument. Phi Delta Kappan, 71, 6 9 2 - 6 9 7 .

Tsai C-C (1998a) Science learning and constructivism. Curriculum and Teaching, 13, 31 - 52.

Tsai C-C (1998b) An analysis of Taiwanese eighth graders' science achievement, scientific epistemological beliefs and cognitive struc­ ture outcomes after learning basic atomic theory. International Journal of Science Education, 20, 413 - 425.

Tsai C-C (1998c) An analysis of scientific epistemological beliefs and learning orientations of Taiwanese eighth graders. Science Education 82, 473 - 489.

Tsai C-C (1999) Content analysis of Taiwanese 14 year olds' informa­ tion processing operations shown in cognitive structures following physics instruction, with relation to science attainment and scientific

epistemological beliefs. Research in Science & Technological Education, 17, 1 2 5 - 138.

Tsai C-C (2000a) Relationships between student scientific epistemo­ logical beliefs and perceptions of constructivist learning environ­ ments. Educational Research, 42, 193 - 205.

Tsai C-C (2000b) The effects of STS-oriented instruction on female tenth graders' cognitive structure outcomes and the role of student scientific epistemological beliefs. International Journal of Science Education, 22, 1 0 9 9 - 1 1 1 5 .

Tsai C-C (2001a) The interpretation construction design model for teaching science and its applications to Internet-based instruction in Taiwan. International Journal of Educational Development, 2 1 , 401 - 4 1 5 .

Tsai C-C (2001b) Probing students' cognitive structures in science: The use of a flow map method coupled with a meta-listening technique. Studies in Educational Evaluation, 27, 257 - 268.

Tsai C-C & Huang C-M (2001) Development of cognitive structures and information processing strategies of elementary school students learning about biological reproduction. Journal of Biological Education, 36, 21 - 2 6 .

Wandersee J H, Mintzes J J & Novak J D (1994) Research on alterna­ tive conceptions in science. In Gabel D L (Ed.), Handbook of Research on Science Teaching, pp. 177 - 210. New York, USA: Macmillan. White R T & Gunstone R F (1989) Metalearning and conceptual

change. International Journal of Science Education, 11, 577 - 586. Wilson F A, O'Scalaidhe S & Goldman-Rakic P (1993) Dissociation of

object and spatial processing domains in primate cortex. Science, 260, 1 9 5 5 - 1958.

Yip D Y (1998) Children's misconceptions on reproduction and impli­ cations for teaching. Journal of Biological Education, 33, 21 - 26. Chin-Chung Tsai is a Professor at Institute of Education and Center for Teacher Education, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 300, Taiwan. Tel. +886 3 5731671; Fax. +886 3 5738083; Email: cctsai@cc.nctu.edu.tw. Chao-Ming Huang is a doctoral student at Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan.

A Science Teaching Scholarship

The Hertfordshire Science Teaching Scholarship is sponsored jointly by John Murray (Publishers) and

Don Mackean (biology author). The award is administered by Hertfordshire County Council but is open

to applicants from all parts of the United Kingdom. The award is worth up to £7000, the bulk of which

must be used to pay for a teacher's replacement while he or she is seconded to work on a project.

The scholarship is offered to science teachers of at least 3 years' experience, working in secondary

schools, middle schools or colleges of further education, or to teachers in primary or special schools with

an interest in science teaching. The scholarship is intended to give teachers time off to develop a project

which will be of benefit to their school, college, region or science teaching in general.

The award enables a teacher to apply for secondment for one term, or an equivalent period of time —

e.g. one or two days a week over a longer period.

The scholarship will pay for the teacher's replacement for this period and also offers a grant of up to

£500 to cover expenses for travel, photocopying and the purchase of materials and equipment.

The project must be one which is intended to make the teaching of science more effective by producing

and testing new ideas, rather than academic research into a science subject or educational theory.

For full details and application forms, write to the Science Adviser,

Wheathampstead Development Centre, Butterfield Road, Wheathampstead, St. Albans, Herts. AL4 8PY.

The closing date for applications is February 1" 2003