外文翻譯--靈活性和計(jì)算機(jī)輔助評(píng)價(jià)技術(shù)-展示頁(yè)
2025-05-27 06:21本頁(yè)面
【正文】 er concentrates specifically on the assessment of puterbased IT skills from the developer’s viewpoint and illustrates the inclusion of flexibility in the development of such assessors with examples from the author’s experience. 2. GENERAL MODELS OF LEARNING AND ASSESSMENT There are many different models of learning which have been developed over the years, some of which are discussed in Domjan (1998). A good summary of many of the wellknown models can be found in Kearsley (1998). The model which many authors cite in the context of puter assisted assessment is Bloom’s taxonomy, Bloom (1956). He and his mittee defined a hierarchical model of learning and assessment where higher levels of the model relate to higher skills. Level 1 Knowledge The ability to remember and recall previously memorized information, for example, to know facts, methods, principles,concepts and procedures. Level 2 Comprehension The ability to grasp the meaning of material, for example, by summarising material or by predicting future trends. This level involves such processes as translation, interpretation and estimation. Level 3 Application The ability to apply knowledge and basic understanding to new situations using such rules, methods and principles as the situation requires. Level 4 Analysis The ability to break down material into its ponent parts, understanding the relationship between each of the parts. Level 5 Synthesis The ability to be able to create a new object from a set of requires planning as well as analysis skills. Level 6 Evaluation The ability to judge the value of material based on specific the Bloom hierarchy, the higher the level of learning and assessment, the greater the flexibility offered to candidates in tests and exercises and the greater the flexibility required of the assessment system. Thus puterised assessors which assess higher levels of the hierarchy are more plex and more costly to produce. At the lower levels assessment returns either correct or incorrect and there is little information available to allow meaningful feedback to candidates. Consider an MCQ to test whether a candidate knows what city is the capital of France. An answer of Paris would be marked correct and anything else incorrect. For an incorrect answer the only feedback which could be given would be to give the correct answer and,possibly, an indication of why the answer chosen was wrong. At higher levels the assessment is graded for degrees of correctness and there is a considerable amount of information which can be used for meaningful feedback to candidates. Consider a question asking for a proof ofa mathematical theorem. An assessor would produce an assessment based on how close the answer was to the correct answer, taking into account the method used. Feedback would consist of identifying the parts of the answer which were incorrect and feeding this information back to the candidate with explanatory ments. Thus at the lower levels exact matching algorithms are normally used whereas at the higher levels approximate matching algorithms are required which are more plex and slower. It is the use of these approximate matching algorithms which distinguishes the assessment of higher level skills from lower level skills. Another major difference between the assessment of higher and lower level skills is the data which is assessed. Lower level exercises almost always assess the oute of the exercise, for example, the formula typed into a spreadsheet cell as part of a spreadsheet exercise. Higher level skills can also be assessed on the oute of an exercise
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