Addressing the assessment challenge with an online system that tutors as it assesses
Addressing the assessment challenge with an online system that tutors as it assesses
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DOI：
10.1007/s11257-009-9063-7
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发表时间：
2009-08-01
影响因子：
3.6
通讯作者：
Koedinger, Kenneth
中科院分区：
文献类型：
--
作者：
Feng, Mingyu;Heffernan, Neil;Koedinger, Kenneth
美国各地的中学教师被要求使用形成性评估数据(Black和Wiliam 1998a，b；Roediger和Karpicke 2006)来指导他们的课堂教学。与此同时，美国政府《不让一个孩子掉队》法案的批评者称该法案为“不让一个孩子不受考验”。此外，批评人士指出，评估学生所花费的每一小时，就意味着失去了一小时的教学时间。但是，一定要这样吗？如果我们更好地将评估融入课堂教学中，让学生在考试中学习会怎么样？我们开发了一种方法，可以在学生无法独立解决的练习评估项目上提供即时辅导。我们的假设是，我们可以通过不仅使用关于学生测试项目是否正确的数据，而且通过使用关于学生在教学帮助下解决测试项目所需努力的数据来实现更准确的评估。我们在ASSISTment系统中整合了援助和评估。该系统通过向学生提供指导，同时向教师提供对学生能力的更详细评估，帮助教师更好地利用他们的时间，这在目前的方法下是不可能的。我们评估学生数学水平的方法是使用我们的系统通过与学生互动收集的数据来评估他们在年终高风险州考试中的表现。我们的结果表明，通过利用交互数据，我们可以可靠地更好地预测学生的年终考试成绩，并且仅基于交互信息的模型比仅使用关于测试题的正确性信息的传统评估模型做出了更好的预测。
Secondary teachers across the United States are being asked to use formative assessment data (Black and Wiliam 1998a,b; Roediger and Karpicke 2006) to inform their classroom instruction. At the same time, critics of US government's No Child Left Behind legislation are calling the bill "No Child Left Untested". Among other things, critics point out that every hour spent assessing students is an hour lost from instruction. But, does it have to be? What if we better integrated assessment into classroom instruction and allowed students to learn during the test? We developed an approach that provides immediate tutoring on practice assessment items that students cannot solve on their own. Our hypothesis is that we can achieve more accurate assessment by not only using data on whether students get test items right or wrong, but by also using data on the effort required for students to solve a test item with instructional assistance. We have integrated assistance and assessment in the ASSISTment system. The system helps teachers make better use of their time by offering instruction to students while providing a more detailed evaluation of student abilities to the teachers, which is impossible under current approaches. Our approach for assessing student math proficiency is to use data that our system collects through its interactions with students to estimate their performance on an end-of-year high stakes state test. Our results show that we can do a reliably better job predicting student end-of-year exam scores by leveraging the interaction data, and the model based on only the interaction information makes better predictions than the traditional assessment model that uses only information about correctness on the test items.