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Parameters affecting task difficulty and language performance CNaVT project

Parameters affecting task difficulty and language performance CNaVT project. CNaVT background. Functional permance based perspective within TBLA paradigm Needs analysis

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Parameters affecting task difficulty and language performance CNaVT project

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  1. Parameters affecting task difficulty and language performanceCNaVT project

  2. CNaVT background • Functional permance based perspective within TBLA paradigm • Needs analysis • TLU Domain analysis: identifying language tasks through observations, expert opinions, sampling materials, learners’ experiences • - How to cluster endless list of tasks? - How to define the complexity of the tasks? - How to equate tasks in order to generalise from task performance?

  3. What is a task? PURPOSE Action Language TASK REALITY Interaction partners Language user Setting

  4. What is a task? REALITY PURPOSE Language user Interaction partners Action Language TASK Setting

  5. Parameters PURPOSE Language user Topic familiarity Background knowledge Interaction partners Acquaintanceship Relationship Number of senders/ addressees LANGUAGE Text type Genre Length Redundancy Vocab Syntax Cohesion&coherence Register Tempo Pronounciation Abstracteness/ concreteness Setting Location Channel Time External sources ACTION Cognitive procvessing Activity Skills involved

  6. Empirical research Can we find empirical evidence to prove the extrapolation of performance across tasks being characterized as similar according to our configuration of parameters?

  7. Research – design Extra research tasks in live examinations 2005 Social (PTIT) & societal exam (PMT): two ‘similar’ tasks (identical configuration) H: candidates will be successful (or not) in both tasks Professional (PPT) & academic exam (PAT): two ‘different’ tasks (identical configuration except for one parameter) H: there will be sufficient variation in successfulness on both tasks

  8. Research tasks in PMT and PPT • PMT - Societal exam (identical configuration of parameters) 1. Listening to advertisements about mobile phones and choosing the best option 2. Listening to advertisements about cars and choosing the best option • PPT - Professional exam (varying parameter: combination of skills) 1. Writing an invitation - 3 skills: listening + reading + writing 2. Writing an invitation - 2 skills: reading + writing

  9. PMT research task scores

  10. PPT research tasks scores

  11. Results PMT and PPT • PMT Car task m = 78,2% sd = 17,6 Telephone task m = 84,1% sd = 16,2 • PPT Two skills task m = 87,1% sd = 14,3 Three skills task m = 87,4% sd = 14,4

  12. Results PMT and PPT • PMT: r = .435 (p<0.01); N = 691 • PPT: r = .503 (p<0.01); N = 265 • PMT: more variation than expected, but highest correlation when compared across all tasks in this exam. • PPT: less variation than expected, not the highest correlation

  13. Results PMT Success tel task = 6/9 (67%) Success car task = 5/8 (63%)

  14. Results PPT Success 3 skills task = 8/12 (67%) Success 2 skills task = 7/10 (70%)

  15. Conclusions • Impact of parameter manipulation on task complexity might differ across proficiency/performance levels • Manipulating one parameter alone might not impact on task difficulty at higher levels • It is not clear whether we should discretely measure the effect of single parameters or we should focus on the effect of different configurations

  16. Future research • How are we going to decide which parameters are worth investigating? • In what way are we going to investigate their effect? • How do we control variables except the one under investigation? • Do we need to accommodate proficiency/performance level as a variable in the design? • How are we going to share information about the effect of parameters and build towards a synthetic framework?

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