Recommender systems are expected to promote a student-centered teaching and learning environment. The age of information abundance has proven to need such systems. Recommender systems have been used to recommend learning items related to students’ research interests. Serendipity has also made its way into the academic environment, as systems recommend items that are useful and surprising to learners. Understanding user workload is important for students who use serendipitous recommender systems. In this research, we investigate various user interfaces for academic recommender systems by looking at students who are attempting to obtain serendipitous recommendations for their academic tasks. The study was evaluated on the NASA task load index (NASA-TLX). Our priority was to understand the mental, physical, and other workload attributes that can change when students seek serendipitous recommendations. We studied Mendeley, Google Scholar, Academia.edu, and ResearchGate. Our study found no substantial serendipitous recommendations observed by the users, but a few traces of serendipitous experiences were observed. Further, no substantial workload was detected in using the systems. However, the recommender system did create different user experiences across repeated sessions. Further, a diverse range of task loads is associated with the recommenders used in academia, from mixed designs with rich user controls to very few controls. This research provided us with insights that can be used to help designers incorporate and accommodate new features and take calculated risks when designing serendipitous education technology.

推荐系统有望促进以学生为中心的教学环境。信息时代已经证明需要这种系统。推荐系统已被用来推荐与学生的研究兴趣有关的学习项目。机缘巧合也进入了学术环境，因为系统会推荐对学习者有用且令人惊讶的项目。对于使用偶然推荐系统的学生来说，了解用户的工作量很重要。在这项研究中，我们通过查看试图为其学术任务获得偶然推荐的学生，研究了学术推荐系统的各种用户界面。该研究是根据NASA任务负荷指数（NASA-TLX）进行评估的。我们的首要任务是了解精神，身体，和其他工作量属性，当学生寻求偶然的建议时，这些属性可能会改变。我们研究了Mendeley，Google Scholar，Academia.edu和ResearchGate。我们的研究发现用户未观察到任何实质性的意外建议，但是观察到了一些意外的经历。此外，在使用系统时未检测到实质性的工作量。但是，推荐系统在重复的会话中确实创造了不同的用户体验。此外，从具有丰富的用户控件的混合设计到很少的控件，各种各样的任务负荷与学术界使用的推荐程序相关联。这项研究为我们提供了见解，可用于帮助设计师在设计偶然的教育技术时融入和适应新功能，并承担已计入的风险。