Educational recommender systems (RS) have become widely popular with the paradigm shift to online learning and the availability of a wide variety of learning resources. Educational RS in various education platforms use a wide variety of filtering techniques. This has led to the development of multiple types of RS. Context-aware recommender systems (CARS) are identified as an emerging type of RS that uses users context for filtering recommendations, which makes recommendations more relevant to the user s current situation. CARS may face initial distrust compared to other RS due to the additional automation layer of context awareness and the use of more user data. Therefore, we conduct a survey-based study to find differences in user trust and perception between CARS and other RS. In the study, users viewed examples of CARS and RS. The results show that users have significantly lower trust in CARS compared to RS.