Service-Based Applications (SBAs) have become increasingly pervasive. These applications rely on the third-party services available on the cloud, and services must be aware of and adapt to their changing contexts in highly dynamic environments. SBAs with context-aware capabilities have provided the users with personalized services based on their user's (intrinsic) and device's (extrinsic) contextual information, as well as the Quality of Services (QoS). The correctness of service substitution in runtime adaptation is substantial for the continuity of user activity on the system. In Mobile Cloud Learning (MCL) environment most works only focus on intrinsic context factors such as learner's profile, learner's location, etc. We then introduce a comprehensive Dynamic Service Adaptation of Context-Aware Mobile Cloud Learning (DACAMoL), which is designed to reason for both contextual factors and QoS in service discovery, ranking, and selection. The framework represents the contextual information, service descriptions, and QoS using a semantic-based approach to improve the correctness of service substitution. In this paper, we present a quasi-experiment study to demonstrate the DACAMoL framework with a mobile app called Mudahnya BM. Mudahnya BM is a learning app to learn basic knowledge of Malay language that build using RESTful backend services. The study involved 30 participants and 33 randomized scenarios tested using One-Sample Wilcoxon Signed Rank test. The results show significantly better service substitutions with 32 out of 33 educational services are correctly adapted (i.e. 95% of the population).