What's in a Prerequisite? A Mixed-Methods Approach to Identifying the Impact of a Prerequisite Course.
What's in a Prerequisite? A Mixed-Methods Approach to Identifying the Impact of a Prerequisite Course.
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DOI：
10.1187/cbe.16-08-0260
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Sandholtz JH
中科院分区：
文献类型：
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作者：
Sato BK;Lee AK;Alam U;Dang JV;Dacanay SJ;Morgado P;Pirino G;Brunner JE;Castillo LA;Chan VW;Sandholtz JH
一门必修课的影响使用了定量数据(考试数据用“熟悉度”量表分析)和定性数据(调查和半结构化访谈)两种数据进行了检验。这些数据肯定了最近项目的变化，并强调了在课程开发中收集和分析学生反馈的重要性。尽管本科科学、技术、工程和数学课程中的必修课程无处不在，但以数据驱动的方式评估它们的价值的努力一直很少。使用定量和定性数据，我们在微生物学讲座和实验室课程配对的背景下检查了先决条件的影响。通过访谈和在线调查，学生们强调了必修课的一些积极属性，包括它们在知识获取中的作用，以及负面影响，例如可能不必要地增加了获得学位的时间和增加了教育成本。我们还确定了一些个人参加或不参加必修课的原因，其中许多与学生的学习无关。在我们的特定课程中，学生们不相信微生物学讲座课程会影响实验室的成功，这与我们使用先前建立的“熟悉度”量表对实验室课程表现的分析一致。这些结论强调了收集和分析学生反馈的重要性，并将这些数据与量化的表现指标进行三角测量，以评估科学、技术、工程和数学课程的状况。
The impact of a prerequisite course was examined using both quantitative (exam data analyzed with a “familiarity” scale) and qualitative (surveys and semistructured interviews) data. These data affirmed a recent program change and highlighted the importance of collecting and analyzing student feedback in development of curricula. Despite the ubiquity of prerequisites in undergraduate science, technology, engineering, and mathematics curricula, there has been minimal effort to assess their value in a data-driven manner. Using both quantitative and qualitative data, we examined the impact of prerequisites in the context of a microbiology lecture and lab course pairing. Through interviews and an online survey, students highlighted a number of positive attributes of prerequisites, including their role in knowledge acquisition, along with negative impacts, such as perhaps needlessly increasing time to degree and adding to the cost of education. We also identified a number of reasons why individuals do or do not enroll in prerequisite courses, many of which were not related to student learning. In our particular curriculum, students did not believe the microbiology lecture course impacted success in the lab, which agrees with our analysis of lab course performance using a previously established “familiarity” scale. These conclusions highlight the importance of soliciting and analyzing student feedback, and triangulating these data with quantitative performance metrics to assess the state of science, technology, engineering, and mathematics curricula.