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Contents : Curriculum Development in Econometrics and Its Use in ETC2400 G. Forchini November 2005 Introduction The development of the Econometrics curriculum has changed drastically in the last thirty years. Some of these changes reflect general developments: e.g. the acquisition of knowledge is longer regarded as the search-for-the-truth but as learning skills that are appropriate for several occupations. Others are more discipline specific. Econometrics can be regarded as a discipline of both economics and statistics and it is thus in a very peculiar situation being on the border between social sciences and sciences. The aim of this report is that of conceptualizing these changes related them to existing learning paradigms and answer the questions asked in assignment one. A starting point: the three domains model of curriculum Barnett Parry and Coate (2001) have suggested a model of curriculum based on thee domains: knowledge (i.e. components that are discipline-specific) action (components acquired through doing) and self (components that develop an identity for the subject). The interactions among these domains and the ways they evolve over time reflect changes in the way academia and society view the process of learning. The econometric curriculum has traditionally followed the statistic curriculum and focused on the knowledge and action domains (e.g. Snee (1993) who refers to 1 them as knowledge and skills components respectively). The self domain (e.g. Snee (1993) who refers to it as attitude and desire component) has not been given much weight. Econometrics seems to follow the science and technology schema (Barnett Parry and Coate (2001)) rather than the arts and humanities schema to which econometrics should belong being part of economics. However even regarded as part of statistics econometrics is not science because statistics is not science. As Watts (1991) puts it: the important fundamental concepts of statistics are quintessentially abstract . This is a characteristic certainly in common with mathematics but good statistics is not equated with mathematical rigor or purity but is more closely associated with careful thinking (Hogg (1991)). Econometrics is moving away from this traditional curriculum in an attempt to create value for it. At least at the undergraduate level econometrics is abandoning its formal content and is placing greater emphasis on problem solving understanding and modelling variation. Demonstration of practical usefulness is what motivates students to learn and retain statistical ideas (Love (2000)). The emphasis is turning towards the process of summarizing and interpreting information and of asking appropriate questions. Smith (1998) writes: Statistical reasoning should take precedence over statistical methods . This is reflected in the fact that textbooks now focus on the econometric/statistical ideas without the mathematical formalism that was imposed in the past. Predominant learning theories Cognitivism has been at the dominating paradigm in econometrics from the start. The traditional structure of units like ETC2400 has been as follows: students listen to the lecturer read the textbook and then do homework problems. Even though the lecture approach is still dominant in econometrics lecturers have come to realise it disadvantages the main one being that this approach is fundamentally subject-based rather than problem-based and the focus is on learning 2 statistical concepts not on the process of using statistics to solve problems (Boyle (1999)). Elements of constructivism have started to creep into introductory and intermediate econometrics at the tutorial level. A promising teaching technique that may alleviate some of the organizational issues is the implementation of a constructivist approach whereby students actively construct their knowledge through activation of previously learned material. (Seipel and Arpigian (2005)). Moreover instructors should be able to link current topics with other material that will help the students comprehend more than just the process but the reason for its use . (Seipel and Arpigian (2005)) The emphasis on relevance of econometric techniques has taken different forms: 1) The use of more meaningful data for examples and homework exercises has been stressed by among others Smith (1998) Ludlow (2002) 2) Federer (1978) Snee (1993) Love (2000) argue for the use of projects where students choose the relevant research questions relating to a problem of interest obtain data to help answer the questions and finally present their results to fellow students. 3) The course focus has been shifted from mathematical explanation to statistical reasoning (e.g. Smith (1998)) and additional emphasis has been placed on the use of statistical software (e.g. Seipel and Arpigian (2005)). For an opinion against the use of statistical software see Kham

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