Scalise, K.; Bernbaum, D.J.; Timms, M.; Harrell, S.V.; Burmester, K.; Kennedy, C.A.; Wilson, M.: Adaptive technology for e-learning : principles and case studies of an emerging field (2007)
0.00
0.0025241538 = product of:
0.015144923 = sum of:
0.015144923 = weight(_text_:in in 1323) [ClassicSimilarity], result of:
0.015144923 = score(doc=1323,freq=16.0), product of:
0.059380736 = queryWeight, product of:
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.043654136 = queryNorm
0.25504774 = fieldWeight in 1323, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.046875 = fieldNorm(doc=1323)
0.16666667 = coord(1/6)
- Abstract
- This article discusses the rapidly emerging field of computer-based assessment for adaptive content in e-learning (National Research Council, 2001), which we call differentiated e-learning. In e-learning products, a variety of assessment approaches are being used for such diverse purposes as adaptive delivery of content, individualizing learning materials, dynamic feedback, cognitive diagnosis, score reporting, and course placement (Gifford, 2001). A recent paper at the General Teaching Council Conference in London, England, on teaching, learning, and accountability described assessment for personalized learning through e-learning products as a quiet revolution taking place in education (Hopkins, 2004). In our study, we examine approaches for the use of assessment evidence in e-learning in four case studies. The products in the case studies were selected for exhibiting at least one exemplary aspect regarding assessment and measurement. The principles of the Berkeley Evaluation & Assessment Research Center Assessment System (Wilson & Sloane, 2000) are used as a framework of analysis for these products with respect to key measurement principles.
