Design and Development Research: Deploying Adaptive Learning Environments to Overcome Background Deficiencies and Facilitate Mastery of Computer Engineering Content

许多打算主修计算机工程的学生并没有坚持通过数字逻辑课程的介绍序列，这些课程是美国每一个经认可的计算机工程课程的一部分，这些学生往往缺乏必要的先决条件，由于他们不同的背景和准备水平。大学入门课程的班级规模很大，难以容纳每个学生的背景知识和技能，使这一问题更加复杂。此外，电子学习环境有可能通过远程授课接触到广泛的受众，从而进一步扩大这一方法的影响。这使得社区学院和四年制大学能够通过允许学生在转学前远程完成较低水平的计算机工程课程，从而实现真正的二加二转学计划。本计画中的适应性学习材料将被开发用于数位电路入门课程与逻辑设计课程。这两个课程都包含实验室的组成部分，也将解决这项工作。这些材料将被开发为独立的学习模块，可用于不同的学院，时间表不同（例如，学期与季度）。这些材料将包括一个统一的、自适应的学习环境，可以使用任何课程管理系统（例如，Moodle、Desire2Learn、Blackboard），以消除对专有软件的需求。随附的实验套件将以低成本、便携式的形式促进动手学习，以促进远程交付，并消除提供有意义的实验室体验的财务障碍。 这些材料将在不同的机构进行测试：蒙大拿州立大学，一个博士学位。授予，研究活跃的大学，MSU比林斯，4年制非研究型大学，平头谷社区学院，蒙大拿州最大的2年制社区学院，和萨利希库特奈学院，部落学院证明2年和4年的学位美洲原住民。这种广泛的参与者将使团队能够评估自适应学习材料的有效性，同时考虑学生背景和学生学习经验之间的关系。将使用定量和定性措施来评估适应性学习模块。将收集学生表现的直接衡量标准，包括自适应模块中未评分的测验尝试次数、模块考试成绩结束时、完成模块所需的时间以及模块中每个难度级别所花费的时间。调查将用于收集学生对自适应学习环境的满意度，其中包括他们对内容难度的印象反馈，与相同学分的其他课程相比的工作量，以及他们对材料个性化的感觉。将收集学生的人口统计信息，包括性别，种族，年龄，社会经济背景，ACT成绩和获得的大学学分。这些信息将用于了解不同群体如何使用课程材料并从中受益。焦点小组还将与密歇根州立大学的学生参与者进行，以收集学生学习和对电子学习系统的态度的定性数据。我们会透过电话访问其他参与院校的学生。这次评估的临时结果将用作反馈，以加强适应性学习系统，以适应更广泛的参与者。

Many students who intend to major in computer engineering do not persist through the introductory sequence of digital logic courses that are part of every accredited computer engineering program in the U.S. These students often lack the necessary prerequisite knowledge due to their varied backgrounds and incoming preparedness levels. This problem is compounded by the large class sizes of introductory college courses, which make it difficult to accommodate the background knowledge and the skills of individual students. Additionally, an e-learning environment has the potential to reach a broad audience through remote delivery, thus further increasing the impact of this approach. This enables community colleges and 4-year universities to move toward a true two-plus-two transfer program by allowing students to complete lower-level computer engineering courses remotely before transferring. The adaptive learning materials in this project will be developed for use in an introductory digital circuits course and a logic design course. Both of these courses contain laboratory components that will also be addressed by this work. The materials will be developed as self-contained learning modules that can be used at different colleges with varying schedules (e.g., semester vs. quarter). The materials will consist of a unified, adaptive learning environment that can be implemented using any course management system (e.g., Moodle, Desire2Learn, Blackboard) to eliminate the need for proprietary software. An accompanying lab kit will facilitate hands-on learning in a low-cost, portable form factor to facilitate remote delivery and eliminate the financial barrier of offering a meaningful laboratory experience. The materials will be tested at a diverse set of institutions: Montana State University, a Ph.D. granting, research active university, MSU-Billings, a 4-year non-research university, Flathead Valley Community Colleges, the largest 2-year community college in Montana, and Salish Kootenai College, a Tribal College proving both 2-year and 4-year degrees for Native Americans. This broad range of participants will allow the team to assess the effectiveness of the adaptive learning materials while simultaneously considering the relationship between student background and the student learning experience. Both quantitative and qualitative measures will be used to assess the adaptive learning modules. Direct measures will be collected on student performance, including the number of ungraded quiz attempts within an adaptive module, end of module exam scores, time required to complete a module, and time spent at each level of difficulty within a module. Surveys will be used to collect student satisfaction with the adaptive learning environment that will include feedback on their impression of content difficulty, workload compared to other courses of the same credit load, and their sense of how well the material was personalized to their needs. Student demographic information will be collected including gender, ethnicity, age, socioeconomic background, ACT scores and college credits obtained. This information will be used to understand how different groups use and benefit from the course materials. Focus groups will also be conducted with student participants at Michigan State University in order to collect qualitative data on student learning and attitudes toward the e-learning system. Phone interviews will be conducted with students from the other participating institutions. The interim findings from this assessment will be used as feedback to enhance the adaptive learning system in order to accommodate a broader range of participants.