Our study examined one pain point users may have with community college articulation agreements: cross-referencing multiple articulation agreement reports to manually develop an optimal academic plan. Optimal is defined as the minimal set of community college courses that satisfy the transfer requirements for multiple universities a student is preparing to apply to. We recruited 24 California community college transfer students to participate in a research session that consisted of an experiment, survey, and interview. We developed a low-fidelity prototype of a report that contains an algorithmically-generated optimal academic plan. We experimentally compared the prototype to ASSIST, California's official statewide database of articulation agreement reports. Compared to students who used the prototype, students assigned to use ASSIST reports to manually create an optimal academic plan underperformed in optimality mistakes, time required, and usability scores. Moving to our non-experimental results, a sizable minority of students had a negative assessment of counselors' ability and willingness to manually create optimal academic plans using ASSIST. Our last results revolved around students' recommendations for supplemental software features to improve the optimization prototype.

翻译：我们的研究聚焦于社区大学衔接协议给用户带来的一个痛点：需要交叉对照多份衔接协议报告来手动制定最优学业规划。我们将"最优"定义为：能够满足学生准备申请的多个大学转学要求的最小社区大学课程集合。我们招募了24名加州社区大学转学生参与包含实验、问卷调查和访谈的研究环节。研究团队开发了一款低保真原型报告，其中包含算法生成的最优学业规划。通过实验将该原型与加州官方全州衔接协议数据库ASSIST进行对比。相较于使用原型的对照组学生，被分配使用ASSIST报告手动创建最优学业规划的学生在最优性错误、所需时间和可用性评分方面均表现更差。在非实验性结果中，相当比例的少数学生对手动使用ASSIST创建最优学业规划的辅导员的专业能力和意愿持负面评价。最后的研究结果围绕学生对补充软件功能的建议展开，以改进最优化原型系统。