## 6 Conclusions  
The objective of conducting this study was to train a device to predict cognitive load into the future, and for detecting the transition from one level of mental effort to another. Our research findings included the amount of data it took to train a device to accurately detect an individual's level of mental effort into the future, and that deep-learning methods are the most effective algorithms in making accurate longitudinal predictions.  
Earlier studies [11; 49] focused on using physiological measures for measuring overall cognitive load and leveraging machine learning to predict mental effort. Similar to [49], in this study, we focused on data from one student; however, in this study we focused on the efficacy of self-training their device by focusing on merging self-report data and the sensor data to predict their cognitive load in the future and detect transitions from one activity to another. In addition to traditional machine-learning algorithms, we experimented with deep-learning methods and found that these algorithms performed well in predicting mental effort of a student into the future; however, further work is needed in order to better detect transitions in mental effort.  
Despite an abundance of literature documenting meaningful relationships between physiological changes and cognitive load, our analysis showed that predicting mental effort was not as straightforward as making predictions around physical processes such as exercise and sleep. It is unlikely that we will find a single model that will work well for everybody, and so the EduFit framework will necessarily rely on the willingness of an individual to train their own device with respect to their lifestyle and individual interpretation of their mental effort. Our models suggested that the first 30% of the data, which equates to 33 activity transitions over approximately 28 h of data collection, was sufficient to train a deep-learning model to make useful predictions for an additional 65 h when the measures were sampled at 1 measurement per second. Since these data were collected across a semester-long (approximately 3 months) timeframe, we feel reasonably confident that deep learning may represent a promising approach for fusing self-report and sensor data to create a useful mental-effort tracking system. However, testing over a longer period of time may be necessary before we can draw conclusions around how this type of model would perform over years, which is more representative of the duration of a program of study.  
Experimental design, J.G. and W.R.; data collection, J.G.; data management, J.G. and A.A.; data analysis, A.A. and W.R.; literature review, N.S. All authors were involved in the writing, final review and approval of the manuscript. All authors have read and agreed to the published version of the manuscript.  
J.G. was supported by the National Science Foundation under grant no. 1742339.  
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Wright State University (06046).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are available upon request by contacting the corresponding author. Data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.