AUDIENCE THEORY -CULTIVATION THEORY - GERBNER.pptx
Effective Methods for Teaching and Assessing Business Applications Programming at Introductory Level
1. Dr Niamh O Riordan
Whitaker Institute J.E. Cairnes School of Business & Economics, National University of Ireland, Galway
Wednesday, 28th November, 2012
2. “The graduating student who professes a complete
inability to write a simple program is commonplace”
(Jenkins, 2001)
„„One wonders [...] about teaching sophisticated material to
CS1students when study after study has shown that they do
not understand basic loops…‟‟ (Winslow,
1996, p. 21).
“Many institutes report drop out rates of 20-40
percents, or even higher, of students on their introductory
programming courses” (Kinnunen and Malbi, 2006)
“Colleges and universities routinely report that 50% or more
of those students who initially choose computer science study
soon decide to abandon it” (ACM/IEEE)
3. Motivation:
Useful and in demand
Challenge:
Not „sexy‟ and quite difficult
Principle: Content
EFFECTIVE
METHODS
FIT!
Student Teacher
Scope: First year undergraduate students already enrolled in their first
Business Application Programming (BAP) course, which combines
lectures and tutorials and is based on Java
4. Not exactly a piece of cake!
“For programmers to develop competence, they
need to have good problem solving skills and a
thoroughly organised knowledge of the
programming language” (Linn and Clancy, 1992)
[cf. on the cruelty of really teaching computer science
Dijkstra (1989)]
The goal:
To move from schemas to scripts and
from comprehension to generation
5. Not always a help…
- Blames the student
- Blames the method
- Misses the point
(Biggs, 1992)
“A teacher‟s job is not to communicate the minutiae of syntax
or the nuances of some particular language, but to persuade
the students that learning to program (and so programming)
would be a good thing” (Jenkins, 2001)
The goal:
From transmission mode to Mr. Motivator
Motivation = Expectancy x Value
6. Not always so self-assured!
“You have to believe in yourself, that's the secret… I had to feel the
exuberance that comes from utter confidence in yourself. Without it,
you go down to defeat” – Charlie Chaplin
The goal:
To embolden the student
Jenkins source: http://www.ics.heacademy.ac.uk/Events/conf2001/papers/Jenkins%20paper.pdfMuratet et al: http://www.hindawi.com/journals/ijcgt/2009/470590/IEEE source: ACM/IEEE-Curriculum 2005 Task Force, Computing Curricula 2005, The Overview Report, IEEE Computer Society Press Kinnunen: http://delivery.acm.org/10.1145/1160000/1151604/p97-kinnunen.pdf?ip=140.203.12.3&acc=ACTIVE %20SERVICE&CFID=211918093&CFTOKEN=45109093&__acm__=1353968463_8f814967945eef2f20d31db9c254a139
Linn and Clancy: Available at http://dl.acm.org/citation.cfm?id=131301 Rogalski & Samurcay, 1990, p. 170:Acquiring and developing knowledge about programming is a highly complex process. It involves a variety of cognitive activities, and mental representations related to program design, program understanding, modifying, debugging (and documenting). Even at the level of computer literacy, it requires construction of conceptual knowledge, and the structuring of basic operations (such as loops, conditional statements, etc.) into schemas and plans. It requires developing strategies flexible enough to derive benefits from programming aids (programming environment, programming methods).Du Boulay (1989) describes five overlapping domains and potential sources of difficulty that must be mastered. These are: (1) general orientation, what programs are for and what can be done with them; (2) the notional machine, a model of the computer as it relates to executing programs; (3) notation, the syntax and semantics of a particular programming language; (4) structures, that is, schemas/plans as discussed above; (5) pragmatics, that is, the skills of planning, developing, testing, debugging, and so on.Robins et al 2003: “a CS1 course should be realistic in its expectations and systematic in its development”
Biggs: Biggs, John. Teaching for Quality Learning at University. OUP / SRHE, 1999. Formula is from Biggs Jenkins: http://www.cs.kent.ac.uk/people/staff/saf/dc/portfolios/tony/doc/other/motivation.pdfRountree et al (2002): the most reliable predictor of success was the grade that the student expected to achieveAttitudes to mistakes and errors: stoppers and movers (cf. Perkins 1989)
On problem based learning: In the future, explicit naming and teaching of basic schemata may become part of computer programming curricula: (Mayer, 1989, p. 156). Also “Deek et al. (1998) describe a first year computer science course based on a problem solving model, where language features are introduced only in the context of the students’ solutions to specific problems. In this environment students in the problem solving stream generally rated their own abilities and confidence slightly more highly than did students in the control stream (receiving traditional instruction). Students in the problem solving stream also achieved a significantly better grade for the course (with e.g. an increase from 5% to over 25% of the students attaining ‘‘A’’ grades)”. “students who are encouraged to actively engage and explore programming related information performed better at problem solving and creative transfer” Robins 2003 Duke et al., 2000: Cited in Costelloe, 2004Costelloe: https://www.scss.tcd.ie/disciplines/information_systems/crite/crite_web/publications/sources/programmingv1.pdfHorizon 2012: http://www.fdi.vt.edu/online-resources/2012-Horizon-Report.pdfBloom, 1956: cf. http://en.wikipedia.org/wiki/Bloom's_TaxonomySoloway and Spoher (1989) are cited in Robin (2003) Ramsden (2002): Ramsden, P. (1992). Learning to teach in higher education. London: Routledge