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A Student Learning Inventoryfor Economics Based on theStudents' Experience of Learning: APreliminary StudyMartin P. Shanahan & Jan H. F. MeyerPublished online: 25 Mar 2010.

To cite this article: Martin P. Shanahan & Jan H. F. Meyer (2001) A Student Learning Inventoryfor Economics Based on the Students' Experience of Learning: A Preliminary Study, TheJournal of Economic Education, 32:3, 259-267

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A Student Learning Inventory for Economics Based on the Students’ Experience of Learning: A Preliminary Study

Martin P. Shanahan and Jan H. F. Meyer

Abstract: The authors present the initial development of a student learning inventory (SLI) that is specific to economics. This approach, which is based on the student experience of learning (SEL) literature, emphasizes aspects of prior knowledge in the learning history of entering first-year students. Preliminary insights from a first SLI suggest that on entry to university, students show considerable variation in their perceptions of what economics is and what economists do. From the SEL perspective, such variation affects student learning. It is argued that continued development of an economic-specific SLI may result in a better understanding of students’ learning engagement with economics and ultimately assist instructors in better understanding student learning difficulties and increase student success in first-year economics.

Key words: first-year economics, student learning JEL code: A22

Economists have long been interested in analyzing the links between teaching inputs and learning outputs, particularly with respect to university economics (Siegfried and Fels 1979). We envisage that an improved understanding of these links will assist in improving the efficiency and effectiveness of the learning pro- duction function and simultaneously, students’ interest in economics (Becker 1997; 2000). Despite numerous efforts to model these links, to date a complete model of student learning in economics remains elusive. In this article, we focus on a neglect- ed area in this field, namely, aspects of students’ prior knowledge, and we empha- size the importance of modeling individual differences in learning engagement.

Previous work has shown that students’ prior knowledge of economics and mathematics, their gender, their compatibility with the teaching environment, and their level of effort exhibit consistent and statistically significant, but gener-

Martin f! Shanuhon is senior lecturer in economics at the University of South Australia (e-mail: martin.shanahan@unisa.edu.au), and Jan H. F. Meyer is the director of the Centre for Learning, Teaching and Research in Higher Education, University of Durham and adjunct Professor at the Uni- versity of South Australia. This research was supported by an Australian 1998 National Teaching Development Grant. The authors are grateful to participants at the 8th European Conference for Research on Learning and Instruction. Goteborg, Sweden; to colleagues from the Universities of Ade- laide and South Australia; and for comments from anonymous referees.

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ally modest, association with assessed outcomes (Becker 1997). Unfortunately, findings such as these are not particularly helpful in assisting university teachers to advise individual students on the specific problems they face when learning and understanding an economic concept. If, however, the university teacher knows, or can identify, how a particular student goes about learning economics, he or she is better able to assist that student. One approach that attempts to exter- nalize variations in students’ learning processes emanates substantively from qualitative studies of students’ experiences of learning and is labeled the “student experience of learning” (SEL) approach.’

INDIVIDUAL STUDENTS AND DIFFERENCES IN LEARNING ENGAGEMENT

In broad terms, three simple questions initially probe learning as seen from the learners’ perspective; namely, the “what?” (What is the learner trying to do?- intention), the “why?’ (Why is the student learning in a particular way?-the motivation behind the intention), and the “how?’ (How is the student going about learning?-the learning process involved) (Meyer 1998). The answers to these questions belie their simplicity.2

The what question, for example, does not refer to the content of the discipline topic, that is, economics. Rather it captures variation within a set of contrasting and purposeful intentions. One such intention might be to accumulate facts and another to make personal sense of the world through an understanding of the dis- cipline. Similarly, there are contrasting forms of both motivation (e.g., intrinsic, extrinsic) and process (mechanical rehearsal versus interconnecting related ideas). How students orchestrate these various intentions, motives, and processes is a classic source of explanatory variation in learning outcomes (Meyer 1991).

One reason economists may not have adopted this approach is the lack of directly observable data on learning processes-how students go about learning. Considerable progress has been made by educational researchers, however, toward developing models of student learning and associated research instru- ments that capture variation in students’ responses to key questions concerning their intentions, motivation, and what learning is (Biggs 1987; Vermunt 1994; Meyer and Boulton-Lewis 1999).

It is clear, given the high attrition and failure rates, that many first-year students perceive university economics as a difficult subject. We argue that one factor affecting students’ success in economics is their preferential or habitual approach to learning on entry to university. The overall aim of our research, therefore, is to identify what a student does when learning economics or, within the framework of Salemi and Tauchen (1987), to develop a model of the learning process.

THE PRESENT PROJECT

In this article, we report on the first stage of the development of a student learning inventory (SLI), aspects of which are subject-specific to economics. Because an initial motivation for this work was to assist students at risk of fail-

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ing, attention was also paid to those learning processes identified in other disci- pline areas to be detrimental to student success. However, we report only those aspects of the test instrument that are specific to economics. More comprehen- sive discussions are provided in Cowie, Shanahan, and Meyer (1997), and Meyer and Boulton-Lewis ( 1999).3

Within the SEL framework, the standard approach involves identifying cate- gories of factors that reflect the what, why, and how of student learning. The approach emphasizes the role of variation in prior knowledge factors; notably factors representing conceptions of learning, epistemological beliefs, learning pathologies, and, in this study, concepts linked to a student’s perceptions of eco- nomics. Underlying this approach is the assumption that inter-individual varia- tion in learning outcomes is explained in terms of variation in what has already been learned, conceptions of what learning is, the intentional focus of learning, associated motivational influences, how learning is contextually perceived, and how it is consequentially engaged.

We assumed that subject-specific prior knowledge can directly influence learning outcomes. For new first-year students of economics, such prior knowl- edge may be attributable to having studied the subject at secondary school. Sec- ond, the production of (new) learning outcome(s) is substantively a function of the learning process. For example, different learning processes are required to comprehend or grasp the meaning of new material compared with simply men- tally storing it for future recall. Such learning processes are not constituted in iso- lation; they are influenced by forms of prior knowledge such as epistemological beliefs and conceptions of what learning is. For example, a student who views learning as the accumulation of facts may learn economics by memorizing mate- rial that is not comprehended and concentrating on what is given rather than what is meant. Furthermore, what students perceive to be, and what they believe econ- omists do is also important in shaping their learning of economics. A more com- prehensive discussion of these issues appears in Meyer (1999).

For the test instrument (the SLI) truly to reflect students’ experiences of learn- ing, the first step involves asking students about their own conceptions of eco- nomics. The crucial issue is to identify whether students vary in their learning processes. In 1997, first-year students from two Australian universities were asked to express their conceptions about economics, and these responses were examined to identify potential learning categories that were economic ~pecific.~

Based on the 1997 survey, we identified three categories as important subject- specific indicators of learning processes. These were labelled (1) economic mis- conceptions, a category that identified student perceptions regarding the aims, objectives, and limitations of economics; (2) economic activity, what students perceived economists do; and (3) economic reasoning, a category identifying stu- dents’ perceptions of economic relationships in the world around them. A fourth category, labelled worth, was constructed with statements that sought to separate those who viewed price as reflecting the market value of a good and those who viewed price as reflecting a good’s intrinsic value. This was in response to the work by Dahlgren ( 1984) that argues this distinction reflects a fundamental divide in student thinking.

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Once the categories were identified, they were then expressed in a subscale: a series of statements that were intended to capture variation in the central mean- ing of each category. The inherent limitation of using a single statement to iden- tify a category of student learning means that generally at least five statements are required to capture reliably the variation within a conceptually discrete source of explanatory variation such as a learning process. Subsequent students were asked to respond to each statement (via a Likert-type response format) on a scale from 1 to 5, where 1 represents strongly disagree, three, neutral, and five, strongly agree. These quantified responses then formed the means by which vari- ation between individual students’ approaches to learning could be measured, and various combinations of learning processes could be identified.

The statements used to construct the different, economically specific sub- scales are provided in Table 1. Recall that with this approach we were looking for variation in students’ responses, rather than correct answers. Statements were designed to require some level of interpretation to elicit students’ percep- tions, rather than a right answer. Students were informed of this before com- pleting the inventory.

Before the commencement of formal teaching in 1998, the SLI was adminis- tered to all 894 incoming first-year business students of economics at the Uni- versity of South Australia. Students were asked to respond to the statements in terms of their most recent school experiences and, where possible, in the context of studying economics. Fifty of the statements sought to elicit students’ reflec- tions on learning, 60 focused on the learning process, and 26 were designed to reveal aspects of the four economic-specific ~ategories.~ The results from the final four categories are the focus of the remainder of this article.

INITIAL RESULTS

The initial and exploratory nature of the work reported here is reflected in the less-than-ideal magnitudes of the coefficient alphas, which are statistical indica- tors of internal consistency or empirical cohesion of a set of item responses that respectively constitute particular subscales. Also, the underlying learning engagement of individuals is complex; even the aggregated subscales of individ- ual statements only reveal, at best, a partial insight into the underlying phenom- enon. This limitation is especially true where the relationships between learning processes, outcomes, and other sources of explanatory variation are being mod- eled in a linear sense (for example, via linear regression) because fundamentally many of the relationships are in fact nonlinear.6 Overall models of learning engagement are unlikely to produce statistically striking results in terms of model fit. This was not being attempted here. At this stage, the aim was to iden- tify learning categories that detect variation in students’ learning processes and that ultimately could be linked to student output^.^

The cohesion of the economic specific categories is reported in Table 1, where values of alpha can range from 0 to I .E The higher the value, the greater the indication that the individual statements are collectively consistent (empiri- cally) with a conceptually one-dimensional measure, such as economic activity.

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TABLE 1 Coefficient Alphas of Economic Categories Affecting Student Learning Processes

in Economics and Their Subscales

Coefficient Economic statements by category alpha M SD

Misconceptions: Economics is responsible for the effect of policy decisions on the

Economics is the study of money. Economics is an exact science. All the people in a country benefit when a high rate of interest is

If an employee gets a pay rise at work. and so do all his friends,

Economics analyzes problems that have little effect on how people

There is generally no logical explanation for things like the currency

economy.

paid on savings.

then they must be better off than before.

live.

crisis in Asia. Activity:

An economist studies people and how they make choices. An economist compares current and historical data to determine

answers to social problems. An economist observes and reviews information and, drawing upon

information from the past, predicts where the economy will be in the future.

An economist uses data and existing models to predict future events. An economist alters models so that they are consistent with the data

Economists analyze complex phenomena in the real world in

The simplification of a complex phenomenon can provide an insight

that has been gathered.

abstract form by means of graphs or equations.

into that phenomenon that would not otherwise be possible.

News reports generally frustrate me because they do not explain why events have occurred.

I often wonder what the economic reasons are behind newsworthy events.

I generally try to make sense out of an argument even if I disagree with its conclusion.

When listening to an argument, I find I can follow more than one contrasting line of reasoning more or less at the same time.

I find it interesting that complex phenomena (such as the weather) can be represented in a mathematical form.

Complex phenomena in the real world (e.g., the weather, international drug trade) can be analyzed in an abstract form by means of graphs or equations.

I like to play around with ideas of my own even if they do not get me very far.

Reasoning:

Worth: The worth of human life can be expressed in dollar terms. The worth of something is the price you pay for it. The price of something can be unrelated to what it is actually worth. What you pay for something is what it is worth. The cost of manufacturing a loaf of bread determines its worth.

0.65 17.5 4.2

2.9 1.1 2.4 1.1 2.5 1.0

2.6 1.2

2.9 1.3

2.2 0.9

2.0 0.9 0.57 25.3 3.2

3.1 1.0

3.5 0.9

4.0 0.7 3.9 0.8

4.0 0.8

3.4 1.0

3.4 0.9 0.50 23.1 3.8

3.0 1.1

3.0 1.1

4.0 0.9

3.8 0.9

3.1 1 . 1

3.1 1.2

3.2 1.1

0.33 12.4 2.8 1.6 1.0 2.2 1.2 4.2 0.9 2.5 1.2 1.9 1.0

____ ~ _ _ _

Note: N = 894.

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By convention, values greater than 0.6 are accepted as adequate for modeling purposes. The reported coefficient alphas for the economic categories range from 0.65 for economic misconceptions to 0.33 for the worth category, which distinguished students who viewed prices as a function of markets from those who did not.

The coefficient alpha provides an important measure of internal consistency. There are, however, other dimensions of reliability that must also be c~nsidered.~ These include stability (measured by test-retest over time), equivalence (using equivalent forms of the test with no time interval), and stability and equivalence (equivalence forms with time interval). These dimensions of reliability are still under review.

The validity of this approach ultimately turns on whether it measures what it is designed to measure-student learning. The content validity of the SLI is founded on the work of Entwistle and Ramsden (1983) and Biggs (1987). Those aspects of this project that relate to students’ perceptions of learning economics, however, have less theoretical support. One test of the validity of our measures is how well they relate with other appropriate criteria. For example, it would be expected that students who did not study economics at school should score more highly (do worse) on a scale of student misconceptions than those who had pre- viously studied economics. Students with such similar prior learning should also do better on a scale representing awareness of what an economist does.

A description of the mean responses of students to the four economic learning categories, grouped by whether students studied economics at school and whether English was a student’s second language, is given in Table 2.’O Of the four categories, only economic misconceptions appeared to separate both groups of students in a manner consistent with expectations. The category examining a student’s understanding of what an economist does also appeared to separate stu- dents who studied economics at school from those who did not, but the extent of separation was less than for economic misconceptions. Interestingly, the remain- ing two categories separated students based on whether English was their second language but not on whether they studied economics at school. One interpreta-

TABLE 2 Mean Responses of Students Categorized by Whether They Studied Economics at School and

Whether English Was Their Second Language

Economics at school No Yes No Yes

English is second language

M SD M SD M SD M SD

Ecomiscon 18.19 3.65 16.36 4.53 16.65 3.95 19.95 4.54 Ecoactiv 24.65 3.05 25.1 3.28 25.17 3.17 25.5 3.45 Ecoreason 23.29 3.11 23.26 3.87 23.16 3.92 23.84 3.33 worth 12.14 2.68 12.40 2.92 11.81 2.48 14.56 3.21 Number of students 418 416 740 154

Note: N = 894. Ecomiscon is economic misconceptions: Ecoactiv is economic activity: Ecoreason is economic reasoning: Worth is market versus nonmarket view of price.

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tion of this result was that these categories reflected the English skills of students and reported more about the English expression used in the subscales than the learning categories themselves.”

There are numerous further checks that need to be done to test the robustness of this approach. For example, factor analysis can be undertaken to examine the inter- relationships between categories. The association between constructed categories and other variables (examination scores, age) will also need to be examined.

The ultimate aim is to identify (and assist) students at risk of failing because of their approach to learning. The final determinant of the usefulness of this approach, therefore, will be whether a link exists between measured learning cat- egories and student outcomes. Such work is continuing.

CONCLUSION

The insights provided by the SEL approach to learning have been sufticient- ly encouraging to warrant further investigation. There is, however, a consider- able row yet to hoe. Several of the categories require (and are currently under- going) further testing. Of the four categories examined to date, those labelled economic misconceptions and economic reasoning appear the most promising. More precision, however, is required in the test instrument, especially with regard to identifying the subject-specific issues that persistently influence stu- dent learning. There is a need to estimate the relative importance of measured learning processes against other input variables. Further work needs to be undertaken to examine other dimensions of validity. Finally, if learning process- es respond to intervention, the impact of such interventions should be pre- dictable and measurable.

This work is an initial response to Sosin, Dick, and Reiser (1997, 101) who argue that “ . . . researchers have not yet developed the definitive learninglunder- standing model for economic education . . .” Although still in the preliminary stages of development, the present study appears to touch on some aspects of the underlying mechanisms of learning outcome production. Students arrive at uni- versity with varying conceptions of what economics is and what economists do. These variations are measurable. If research on the student experience of learn- ing literature is correct, such variation may go some way to explaining variation in students’ results. Student learning inventories thus have the potential to provide insights that complement existing work on student learning in economics, by increasing our understanding of learning processes and thereby bridging the gap between inputs and outputs. A better understanding of the what, why, and how of students’ learning within economics will ultimately assist instructors’ teaching, the help they can offer individual students and ultimately, students’ success.

NOTES

I . The SEL literature emanates from qualitative (phenomenographic) studies of students’ experi- ences of learning in academic contexts. Although the SEL literature has contributed many insights into how students learn, it is also eclectic, borrowing many explanatory constructs from mainstream psychology.

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2. As a starting point to the literature, see Entwistle and Ramsden (1983) and Biggs (1987). 3. Note that it is assumed that by measuring students’ experiences of learning we obtain accurate

insights into what they actually do. The link between subject-specific conceptions (e.g., eco- nomics) and student learning is discussed in more detail in Meyer (1999).

4. First-year cohorts of economics students at the Universities of Adelaide and South Australia were asked to: ( I ) describe how an economist analyses the economy; (2) write a short description of what they thought an “economic analysis” is; (3) explain any influences on their conception of what an economic analysis is; (4) explain their own mental model of economic analysis (if they had one) in terms that someone else would understand; (5) explain the meaning of the statement that “any model of economic analysis should take the following two modes of thought into account: it is a sophisticated process of gathering data that is used to generate hypotheses and a process of hypothesis testing with a view to confirmation andlor rejection.” A content analysis of these (anonymous) written responses revealed considerable variation among students in their conceptions of economic activity; specifically in terms of what economists do, and what consti- tutes an economic model and an economic analysis. The conjecture was that evident misconcep- tions might be attributable to incoming prior knowledge rather than to the formal study of eco- nomics at university. Specific student comments were then identified to form the basis of an item pool from which to construct a coherent subscale of statements by which to measure that learn- ing process category.

5. The student reflections on learning categories are reported in more detail in Meyer and Boulton- Lewis (1999).

6. The preferred (nonlinear) statistical model to preserve the uniqueness of the observed response is based on a multidimensional unfolding analysis (Meyer 1991; 1998).

7. To be useful each category, must, as a minimum, reveal variation in students’ responses. It is inferred that individuals exhibiting different scores differ from each other, particularly those located in the tails of the distribution and that such differences are of consequence, in terms of other categories in the model, and ultimately to student outcomes. Each of the four categories passes this preliminary test.

8. More details on the distributions and coefficient alphas of these and other learning categories are in Meyer and Boulton-Lewis (1997).

9. This section leans heavily on Walstad (1987). 10. Other student groupings were based on gender and the level of mathematics studied at school. Nei-

11. One reason for identifying students for whom English is their second language is that such stu- ther of these groupings revealed differences in mean scores against the four learning categories.

dents may have fewer English skills than their counterparts and thus be more at risk of failure.

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