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The importance of prior knowledge to new learning

P. A. Addison and V. K. Hutcheson
School of Accounting
Curtin University of Technology


Introduction

The focus of Accounting Education in Australia since the Mathews Report (1990) has been on developing a series of skills, capabilities, and understandings, the objective being that students are seriously disadvantaged in a global economy if they cannot deal with uncertainty. Moreover, global business communication is now so complex and technology so fast, that it requires many people from diverse specialties to cooperate with people in different locations. Furthermore, society suffers a disservice if students are narrowly educated or adept at procedural manipulations and technological virtuosity, but inclined to vacillate in the cognitive doldrums if taken beyond procedure or narrow disciplinary specialisations (Postman, 1996, cited in Craig et al, 1999).

Accounting accreditation requirements are important because they specify the need for students to think critically. This means that they must acquire procedural knowledge rather than declarative knowledge (Kurfiss, circa 1995), be deep thinkers, and have a clear understanding of the concepts and principles that underpin the discipline. From day one at university they must acquire concepts upon which to build this knowledge. As Bruner (1974) reminds us:

Man constructs models of his/her world, not only templates that represent what he encounters and in what context, but also ones that permit him to go beyond them. He learns the world in a way that enables him to make predictions of what comes next by matching a few milliseconds of what is now experienced to a stored model and reading the rest from the model (18).
Thus the essence of learning is understanding.

This paper has two objectives. First, to examine the importance of prior knowledge in developing concepts, and second, to propose a constructive form of education that develops the conceptual schema necessary for acquiring critical thinking skills. It also reports on an experiment to teach students in a pre-university accounting course. This experiment used a pre-test post-test design to measure the change in students' prior knowledge during a semester.

The importance of prior learning

Research into short term memory shows that cognitive load affects learning (Cooper, 1998), that the combination of intrinsic load and extrinsic load overburdens the mind and makes learning difficult, sometimes impossible (17), that novices often incur a high cognitive load in trying to solve problems (Sweller, 1988), that certain strategies can help to reduce the load (Larkin et al., 1980), that worked examples help students in mathematics (Cooper & Sweller, 1987), and that using graphics and text, the split attention effect, does not improve student learning (Sweller et al., 1990). This branch of research into memory is concerned with developing appropriate instructional materials. The importance of prior learning and the storage in long term memory of important conceptual frameworks or models is another perspective into research on memory.

Prior knowledge (PK) is defined as 'the knowledge, skills, or ability that a student brings to the learning process' (Jonassen & Gabrowski (1993). Other theorists have also provided vague definitions, using numerous terms to refer to prior knowledge [as current knowledge, world knowledge, expert knowledge and pre-knowledge]. Prior literature reviews (Alexander, Shallert & Hare, 1991; Dochy; Dochy & Alexander, 1995) have also identified the large number of terms available as a problem with the knowledge literature.

Research also indicates that there is a strong relationship between prior knowledge and performance. Because literacy is now essential to commerce and domestic life, the need to acquire good reading and writing skills is urgent. As Kurfiss (1995: 32) notes:

Reading is not simply a matter of absorbing individual words; ...it is a progressive effort to construct a "model of the meaning of the test" (Armbuster, 1984)...Effective readers remain absorbed by texts... poor readers often do not recognize their own failure to understand a word or passage...
This research confirms Resnick's (1981) results that showed that prior knowledge explained a greater amount of variance than any other variable [i]. Investigations using causal modelling techniques also support the importance of prior knowledge. Most studies considered the direct effects, but there are other learning variables related to prior knowledge that are essential for student performance. These include accessibility and availability of information and the structure of prior knowledge. In addition, methods of assessment have been shown to influence the observed effect of prior knowledge on performance. However, misconceptions and inconsistent information has hindered this research.

An important message is that the positive effects of prior knowledge are apparent when objective methods are employed. However, while, superficial methods like familiarity ratings have consistently failed to show a clear relationship between prior knowledge and learning outcomes, a closer examination of these studies often reveals that flawed assessment methods are useful for exploring learning processes that provide explanations for the prior knowledge effect. Thus determining prior knowledge levels should be a primary consideration in designing studies and assessing performance.

Research also highlights the paradoxical nature of prior knowledge: inaccurate knowledge hinders students' development, and lack of it makes it impossible for them to progress (Pintrich, 1993). On the other hand, addressing misconceptions through instruction and alerting students beforehand that new knowledge may be inconsistent with what they already know, helps them to learn (Biemans & Simons, 1994). By contrast, prior knowledge plays a mediating role in generating constructive activity (Chan et al., 1992), and the quality of study materials can affect a student's prior knowledge and indirectly his/her performance (Dochy, 1992). Research also shows that prior knowledge is potentially an important contributing variable in explaining post-test variance (Dochy, 1992).

The critical need to develop long term memory

Despite acceptance that learning occurs by first identifying words, then word meanings, and then by combining sentence meanings to get the meaning of a whole text, reducing learning to a linear process fails to show that language is more complicated and more useful than that. The mind is constantly inferring meanings that are not directly stated by the written words but are nonetheless part of its essential content. Moreover, the mind [an act of intellect] as distinct from memory, identifies with understanding to give knowledge once acquired, maximum durability. The point is that the explicit meanings of a piece of writing are the tip of the iceberg of meaning; the larger part lies below the surface of the text and is composed of a reader's own relevant information (Hirsch, 1988: 34).

Following Miller's (1956) discovery that short term memory lasts for milliseconds but that long term memory lasts from a few seconds to a lifetime, Fillenbaum and Sachs (1966) discovered that people have poor memory for words, but good memory for meaning. Barclay, Bransford and Franks (1972) discovered that initial understanding of a text depends on applying relevant prior knowledge that is not in the text. The constructive hypothesis as it is known, indicates how important prior knowledge is for supplying the meaning of a text. People's experiences are interpreted in classifications that are neither specific nor general if no other constraints are at work [ii]. How can this be so? Once a long piece of text has been remembered the elements are combined into 'chunks' or smaller elements. There's no logic or reasoning behind the exercise: it is a strategy for remembering.

This strategy for remembering is based on the distinction between sensory, working or short term, and long term memory. Together these modes of remembering are integrated to define an information processing model of the human cognitive architecture. A model of this architecture is shown in Figure 1.

Figure 1

Figure 1. Source: Cooper G, (1998:7)

Definitions

Sensory memory is defined as the stimulation of the senses: examples include sights, sounds, smells, tastes and touches. The key characteristic of sensory memory is that it extinguishes rapidly. It also partitions each of the above senses in memory (Cooper, 1998: 2).

Short term or working memory, provides one's consciousness. Its key characteristic is that it helps us to process information by relating how and where one thinks and processes information.

Perhaps the most pervasive feature of human intellect is the limited capacity at any moment for dealing with information. There is a rule that states that we have about seven slots, plus or minus two, through which the external world can find translation into experience. We easily become overwhelmed by complexity or clutter (Bruner, 1974: 18).
Long term memory by contrast consists of the large amount of knowledge and skills that are held more or less in permanently accessible form (Cooper, 1998: 6). This memory is characterised by familiar knowledge like names, dates, the alphabet, heroes, playing games of all sorts, and 'other how to' information, and not so familiar knowledge. Unfamiliar or less familiar knowledge includes concepts of all types, concepts that are isomorphic and allow the possessors to think across a range of disciplines and to see the world from many perspectives. Thus we observe that some knowledge and skills are activated automatically: others less so. Students search their long term memory to establish whether they 'know' or 'don't know.' This is why prior knowledge is crucial.

The idea of schema

A schema functions as a unified system of prior relationships. Because students'[and others'] narrow attention spans confine them to just a few elements at a time, the technique of using surface elements to stand for larger wholes is an essential feature of their mental life. Schemata are necessary instruments for making the surface of what they read connect significantly with the prior knowledge that is withheld from immediate consciousness due to the limits of short term memory.

To pursue the practical importance of this fact let us explain how schemata works. The first breakthrough came when in 1922 Schultz identified the tendency to 'schematize' (Hirsch, 1988: 55) and second, ten years later, when Bartlett showed that everyone constructs memories from their habitual schemata. Bartlett claims that:

An individual does not normally take a situation detail by detail and meticulously build up the whole....Very little of his construction is literally observed. But it is the sort of construction which serves to justify his general impression (cited in Hirsch, 1988: 55)
A schema is a hierarchical information network: its centrality in learning cannot be overstated. It holds the detail and the complexity learned and understood over time, and integrates new content knowledge. The question is, How does knowledge and skill become encoded and stored in long term memory? [iii].

According to Bruner (1974), there are many devices: theories, models, myths, cause and effect accounts, ways of looking and seeing, and ways of thinking. The focus of this paper is on the theory - the concepts or connected collection of concepts that are the means of acquiring a lot within a 'chip.'

A theory avoids the clutter that surrounds explanation and facilitates understanding. It has the advantage of being compact, accessible, and manipulable. This hierarchy includes knowledge, some of which is more significant than the rest, for knowing about some aspect of nature and life. This knowledge is significant because once a student knows it, and has a theory and procedures for putting it together and going beyond it, he/she can reconstruct the less significant knowledge and other aspects that make up the body of knowledge.

[These] models or stored theories of the world that are so useful in inference are strikingly generic and reflect man's ubiquitous tendency to categorize....We organize experience to represent not only the particulars not only the classes of events that have been experienced but the classes of events of which particulars are exemplars (Bruner, 1976: 19).
Theory is thus a way of stating tersely what one really knows without the burden of detail: it is a canny and economical means of keeping vast amounts in mind without having to think about much. This view dates back to Whitehead (1932).

Key elements in learning are first, the interactivity of prior knowledge with the knowledge to be learned, and second [iv], the degree of interactivity required (Cooper, 1998: 14). The higher the interactivity the more difficult the learning. For example, Cooper provides an example of building sentences:

To build sentences that are grammatically correct...one must attend to all the words in the sentence at once while also considering the syntax, tense, verb endings and so on. Grammar is an example of a high element interactive material because to learn it, many elements have to be considered simultaneously.
It is a form of personalising knowledge, explained by Bruner (81) as the act of making the familiar an instance of a more general case, and thereby achieving awareness of it.

Research methods

The aim of this study is to examine students' learning outcomes, first by rigorously stating students' behaviour levels before learning so that any changes can be genuinely attributed to the effects of the instruction. We also want to help students, and to assess ourselves, to provide improvement. At the beginning of the subject, 36 students in two classes were administered a pre-test. Eight weeks into the course, students were again asked to prepare a concept map by applying the concepts used in the pre-test. At the completion of the course students were surveyed about their experiences in the subject.

The students and the study design

The theoretical foundation of the experiment's instructional design was integrating Wittgenstein's language games and recent accounts of the usefulness of constructivist type learning, particularly drawing on or reinforcing prior knowledge and developing a deeper meaning of selected concepts. As a whole the subject was a pedagogical application of the knowledge transforming model of teaching and reinforcing concepts, assumed here to help students understand accounting [domain] knowledge. In constructing the course we took notice of the power and economy of concept learning (Bruner, 1974; Ausubel, 1968).

According to Ausubel, the cognitive structure is hierarchically organised in terms of highly inclusive conceptual traces [past experiences] under which are subsumed past experiences of less inclusive concepts. The major organising principle of these trace elements is progressive differentiation, in which concepts range from greater to lesser inclusiveness 'each linked to the next higher step in the hierarchy through a process of subsumption' (Ausubel, 1968: 25, cited in Addison, 1982). Thus he postulates that learning occurs when potentially meaningful material is absorbed into the cognitive structure and becomes subsumed under a conceptual system which is both relevant and more inclusive. Thus information is given to students and managed by ensuring that subsumers called 'advanced organisers' are given to students.

We decided on Wittgenstein's (1957) description of concept formation as a game because it is a light hearted approach and achieves results. It is used here to underpin the proposed epistemological approach. The essence of Wittgenstein's philosophy is that language consists of:

The meaning of a concept is so inseparably bound to the entirety of practices within the [accounting] language community that no further justification need or indeed can be given. Confusions occur because language and its nature are misunderstood: 'when language goes on holiday.' Thus it is necessary to identify misconceptions rather than develop new theories. It is also necessary to determine how language works (Grayling, 1988: 67).

Language games

Language encapsulates many communication systems practiced by communities like accountants. For example, the language of accounting [the words used] is not like the language of science or physics formulae. Word games capture the essence of Wittgenstein's thoughts because the concept of games cannot be strictly defined or confined: there are board games, racket games, football games, and chess games. Although there isn't any single essence of game-hood, there are a series of overlapping and crisscrossing similarities or family resemblances, meaning that the same name may have similar or even different meanings in different languages. Wittgenstein uses an analogy to express the variety of language:
Think of the tools in a tool chest. There is the hammer, pliers, a saw, a screwdriver, a ruler, a glue jar, nails and screws. The function of the words is as varied as the functions of these objects - there are also similarities (Wittgenstein, 1957:11, cited in Grayling, 1988).
He defines forms of life as those assumptions, practices, traditions, and natural propensities that humans, as social beings share with one another, and which is therefore presupposed in the language they use. As such language is woven into a pattern by the shared outlook and nature of its users. Thus, learning the language of accounting is learning the outlook, the assumptions, and the practices with which the language is inseparably bound and by which its expressions acquire meaning.

Wittgenstein's concept formation consists of a form of mental map. The map requires students to identify a list of concepts for a particular word. Hirsch (1988) used a canary, Cooper (1998) a car. The concepts we chose are given in Appendix A. Our objective was to determine how and whether students were using surface [specialised] or abstract [general] concepts. We defined surface learning as declarative, or rote type learning: "Declarative knowledge [is] acquired through memorization...[such] knowledge is not helpful in solving problems" (Kurfiss: 34). We defined abstract learning as procedural knowledge or doing type learning: "Procedural knowledge is relevant to critical thinking [and] includes knowledge of how information is obtained, analyzed and communicated in a discipline" (Kurfiss: 40).

We also asked students to interpret a five line passage relating to the determination of profit, the objective being to gauge whether they understood what they had learned so far.

The purpose of the experiment was to free students of a surface approach to learning and to promote a constructive learning context. The idea was that students should think rather than rote learn, and be actively engaged with the information they had to study. The tasks we set did not allow them to use textbooks.

Research questions

The aim of the study was to identify what students already know to help us plan the learning environment efficiently, and to compare their learning outcomes with their entering levels. Learning outcomes were examined from three perspectives: (1) as students' subjective experiences, (2) as conceptual change, (3) and as measured by examination. We addressed the following questions:
  1. What were students' subjective learning experiences?
  2. How did students' conceptions of learning improve during the semester?
  3. What learning outcomes were achieved under examination conditions that measured (a) concepts and (b) other content?
The three questions had three different means of assessing learning. First, student self assessment was used to assess their perceptions of their experiences. Second, assessment of cognitive change was determined as part of cognitive learning theory. These approaches are part of the constructivist perspective of learning. The third method of assessment used traditional assessment measures as part of the knowledge transmission paradigm.

Data collection and data analysis

These questions were investigated using different data collection methods. Students' subjective learning experiences were studied as follows:
  1. A survey was administered to ask students about their subjective perceptions of their learning experiences.

  2. A pre-test/post test design was administered to collect data about students' conceptual understanding. Pre-testing of students was a critical element in the evaluation. The pre-test provided data about students' entering prior knowledge level understanding of profit determination, and the depth of understanding of an accounting concept. The post test provided data about students' learning.

  3. The end of semester exam was used to assess student learning - this examination involved reproducing information.

Student profile

Table 1: Information about participants in the study


Number of studentsFemaleMale
Group 1 1789
Group 2 1367
Educational background High School Cert.No High School Cert.
Group 1 017
Group 2 013

Students' background

This subject provides Foundation students with an alternative entry to University. It is a 14 week course as compared to the normal entry student who undertakes two years of study in accounting. In first year the normal entry student learns bookkeeping techniques; second year the student learns the theory of accounting, how to analyse financial statements and how to complete cash flow statements. Thus students are familiar with both the theory and techniques of accounting on completion. Foundation students cannot achieve this standard, given that knowledge of bookkeeping rules are a fundamental requirement for learning accounting.

Results

1. Students' subjective learning experiences

Students perceptions of learning

Students' perceptions of the learning environment are shown in Tables 2, 3, and 4. All items were measured on a Likert scale (1 = Strongly disagree, 5 = Strongly agree).

Objectives of the course
The results in Table 2 indicate that for these items, students were reasonably happy. They also indicate that there were no significant differences between groups.

Table 2: Comparisons of means of Groups 1 and 2 of the perceptions of the objectives of the course

Objectives
and content
Students'
mean 1
Students'
mean 2
Mean
difference
t-TestSig.
(2 tailed)
Question 1 3.78954.0769-2.874-.935.357
Question 2 3.73683.8333-9.6491E-02-.430.670
Question 3 4.26324.1538.1093.474.640
Question 4 3.68423.61546.883E-02.276.784
Question 5 3.73683.6154.1215.444.662
Question 6 3.73684.0000-.2632-.839.410
Question 7 3.68424.0000-.3158-.914.372

Assignments and Assessment
The results shown in Table 3 also indicate that all students were satisfied with the assignment and assessment procedures adopted for the course.

Table 3: Comparisons of means of Groups 1 and 2 of the perceptions of the assignments and assessment

Objectives
and content
Students'
mean 1
Students'
mean 2
Mean
difference
t-TestSig.
(2 tailed)
Question 1 3.94744.1538-.2065-1.057.300
Question 2 4.05264.4615-.4089-1.781.085
Question 3 4.0004.3077-.3077-1.403.173
Question 4 4.0003.5385.46151.451.161
Question 5 3.52634.0769-.5506-2.000.056
Question 6 3.33334.0000-.6667-2.304.033
Question 7 3.83334.0000-.1667-.682.502

Lecturer presentation
Perceptions of lecturer presentation shown in Table 4 were all above 4 out of 5, with the exception of Question 5 for Group 1, which had a mean of 3.7895.

Table 4: Comparisons of means of Groups 1 and 2 of the perceptions of lecturer's presentation

Objectives
and content
Students'
mean 1
Students'
mean 2
Mean
difference
t-TestSig.
(2 tailed)
Question 1 4.15794.4167-.2588-.867.394
Question 2 3.89474.0000-.1053-.457.652
Question 3 4.05264.3333-.2807-.977.338
Question 4 4.15794.0000.1579.610.547
Question 5 3.78954.0833-.2939-.945.353
Question 6 4.36844.33333.509E-02.137.892

The results of the survey suggest that students were happy with the way the course was conducted and the care and attention given by their teacher.

2. Measurement of students' conceptions of learning

Students' conceptions of learning were measured by a) examining the content of their answers to a quotation from their textbook, and b) by having them nominate the characteristics of three concepts, moving from specific to general. The instrument and solutions are given in Appendix A. The evaluation methods were as follows:

Progressive assessment1. 2 x 1 hour tests30 marks
Homework10 marks
Classroom participation10 marks
Final examination50 marks

Classroom participation included the pre-post tests.

Pre-test
The following table provides students' quotation responses [see Appendix A] as follows.

Table 5: Q. Did you understand the quotation?

Student responses YesNoPartlyNot
really
Not
completely
NothingNo
response
Total
Group 1 6-132 1417
Group 2 55--- -313
Total 115132 1730*
*Some students were missing on the day

The following student responses were given in respect of the comments about the quotation:

Q. What did you not understand?

  1. Not really sure
  2. It is not specific enough
  3. I think I still need to study more
  4. The part which said 'most accounting concepts are commonly referred to as conventions (3)
  5. I don't know exactly what this means
  6. Some people make up questions just to do a survey
  7. I don't know where I should put the debit or credit for transactions
  8. The typical characteristics of accounting is different so it do not really specify
  9. It is easy to say what they are but not the characteristics.

Discussion

Pre-test
The two classes used a 1 hour lecture and two-hour seminar format; that is three hour's tuition. It was clear from part A of the pre-test that students did not understand the meaning of the quotation as evidenced by the number of 'no' answers and 'no' responses. In respect of Part B students did not conceptualise car, vehicle, and asset as related concepts; that is they could not visualise 'car' [a specific term] as an abstract term 'asset.' In respect of the test as a whole, students did poorly. It was clear that they had not studied book keeping or other accounting prior to enrolling in this course. Their mean score was 2 out of 6.

Post-test
Five weeks later students were tested again to determine their progress. They were asked to define asset, and to draw a concept map to demonstrate their understanding of an asset. The expectation was that they would define asset in terms of the Statement of Accounting Concept Statement (SAC) 4 definition. Very few did so. We are at a loss to understand why this is so, given that part of their tutorial was concerned with accounting concepts and related theory. Some students did try. Others fell back on specific instances like car, and other related sub-concepts. One possible explanation is that students had English difficulties and did not read the text book. They had been given lecture notes and could have relied on them. Another explanation might be that these students were dismotivated, or had difficulty in settling into their new surroundings. Answers to these questions may be gleaned by the results of the survey of their learning experiences. Whatever the answer, it is clear that the course will have to be tailored more closely to their needs. The results of the post-test were very poor. The mean score for the post-test was 1.64 out of a range of 6.

3. Students' examination results

Table 6 provides means and tests of significance for students' examination results at the end of Semester. All but one student completed the course successfully, but only thirteen students passed the examination.

Table 6: Analysis of student exam results

Exam Student
mean 1
Student
mean 2
Mean
difference
t-TestSig.
(2 tailed)
Question 1 3.26325.9615-2.6984-1.601.128
Question 2 20.00021.3077-1.3077-1.173.256
Question 3 14.631623.1923-8.5607-2.126.050
Question 4 5.10535.7308-.6255-.756.460
Total 43.157957.2308-14.0729


However, students scored well on progressive assessment which included:

  1. Class participation
  2. Homework assignments
  3. Tests (2)
Students adopted 'surface' (rote) learning techniques and were unable to translate them into the knowledge required in the final examination. A logical conclusion is that the students did not have the required literacy skills. On page four we noted that because literacy is now essential to commerce and domestic life, the need for good reading and writing skills is urgent, and they have to be learnt. Most of the students were overseas students with a poor command of the English language. Additionally, the students lacked prior knowledge; very few of them had any knowledge of accounting prior to taking the course. This conclusion is consistent with the comment on page five where we noted that inaccurate knowledge hinders students' development, and lack of prior knowledge makes it impossible for them to progress.

The textbook used for the course may not have been ideal for the teaching of theory. Students' understanding of theory is deficient. We noted on page seven that theory avoids the clutter that surrounds explanation and facilitates understanding. Theoretical knowledge is critical because once a student encodes it, he or she can reconstruct less significant knowledge and other aspects that make up the body of knowledge. It is significant that students learned one particular aspect, cash budgeting (Question 2 in the examination) far better than any other aspect, and their results were significantly higher than for other questions. Cash budgeting is a topic that can be learned in isolation, requiring less prior knowledge than needed for the presentation of a profit and loss statement (Question 3).

Unfortunately, students' knowledge was not personalised and they were not able to move from a familiar instance to a general case.

Conclusion

Students between secondary education and College/University level are increasingly called upon to integrate information. This was our aim in this course. It was only partially successful. Our results showed that students did not become meaning-directed during the semester. We think that personological factors and situational factors explained this. The factor structure underlying the variables was 'fuzzy', unclear and unconsolidated. This is hard to explain.

The results concerning intra-individual development are disappointing. The diffuse factor structure may be explained by a settling in period of 'friction' as students had to adapt to a new learning environment.

Endnotes

  1. See also Tobias, cited in Dochy, who agrees that a considerable amount of the variance of performance is explained by the level of prior knowledge.

  2. We have relied on Hirsch, 1988 for this overview of prior knowledge in memory.

  3. As Kurfiss (26) observes: 'To make its way to memory, knowledge must be acted upon by the learner. In terms of the metaphor of information processing...short term memory is the [active processing of information]. When integrated with prior learning it becomes useable knowledge...stored in long term memory...This knowledge is organized into patterns that provide a context for new information called...schemas.'

  4. Cooper has given his approval for using Figure 1.

References

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Adler, M. J. (1988). Reforming Education: The Opening of the American Mind. Ed. Van Doren, G. New York: Macmillan Publishing Co.

Alexander, P. A., Schallert, D. L. & Hare, V. C. (1991). Coming to terms: How researchers in learning and literacy talk about knowledge. Review of Educational Research, 61, 315-343.

Ausubel, D. P. (1968). Educational Psychology: A Cognitive View. Holt Rinehart and Winston, New York.

Biemans, H. J. A. & Simons, P. R. J. (1994). How to use preconceptions: The contact strategy dismantled. University of Nijmegen: Department of Educational Sciences

Bruner, J. (1974). Relevance of Education. Great Britain: Penguin Education.

Chan, C. K. K., Burtis, P. J., Scardamalia, M. & Bereiter, C. (1992). Constructive activity in learning from text. American Educational Research Journal, 29(1), 97-118.

Cooper, G. (1998, March). An Introduction to applications of Cognitive Load Theory to Instructional Design. A presentation at the Education 1998 Conference.

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Dochy, F, Segers, M. & Buehl, M. (1999). The relation between assessment practices and outcomes of studies: The case of research on prior knowledge. Review of Educational Research, 69(2), 145-186.

Grayling, A. (1988). Past Masters: Wittgenstein. London, Oxford University Press.

Hirsch, E. (1988). Cultural literacy: What every American needs to know. US: Vintage Books.

Jonassen, J. H. & Grabowski, B. L. (1993). Handbook of Individual Differences, learning and instruction. Part VII, Prior knowledge. Hillsdale: Lawrence Erlbaum Associates.

Kurfiss, J (1995). Critical Thinking: Theory, Research, Practice, and Possibilities. Washington: ASHE-ERIC Higher Education Report, 17(2): 1-154.

Larkin, H., Chandler, P., & Sweller, J. (date unknown). The role of Visual Indicators in Dual Sensory Mode Instruction. Educational Psychology.

Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivation beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63, 167-199.

Resnick, L. B. (1981). Instructional psychology. Annual Review Psychology, 32, 659-704.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285.

Wittgenstein, L. (1922). Tractatus Logico-Philosophicus. London, Routledge and Kegan Paul.

Wittgenstein, L. (1957). Philosophical Investigations. Translated by G. E. M. Anscombe. Oxford: Blackwell.

Appendix A

Appendix A was not available at date of creation of this HTML file.
Authors: P. A. Addison and V. K. Hutcheson
School of Accounting
Curtin University of Technology
GPO Box U1987, Perth WA 6845
Auhor for correspondence: P. A. Addison
addisonp@cbs.curtin.edu.au Phone: (08) 9266 7567

Please cite as: Addison, P. A. and Hutcheson, V. K. (2001). The importance of prior knowledge to new learning. In A. Herrmann and M. M. Kulski (Eds), Expanding Horizons in Teaching and Learning. Proceedings of the 10th Annual Teaching Learning Forum, 7-9 February 2001. Perth: Curtin University of Technology. http://lsn.curtin.edu.au/tlf/tlf2001/addison.html


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