## 5 Discussion  
Previous work has demonstrated both the utility and limitations of the current state of the art of the EduFit system: using wearables to facilitate near real-time monitoring of mental effort. A cross-sectional study with multiple participants showed that learning activity, as well as whether or not a participant was in a high mental effort state, could be predicted accurately, and that ensemble models such as random forest showed the greatest utility for prediction, even when training sets were comparatively limited [11]. However, in all cases, the attempt to predict a person's mental effort using another person's physiological data was met with limited success, even when the data were normalized with respect to the individual's unique average and variability. With regards to moving forward with the effort to develop an automated tracker for mental effort, this showed that it is currently unrealistic to develop and implement a pretrained universal algorithm for the prediction of mental effort; individual variability in physiology and its relationship with mental effort is too great for such an algorithm to work well. We therefore began this study by hypothesizing that an individual could train their own device through the fusion of physiological data and manual entry of self-reported mental effort on various tasks through a web or smartphone application. The key question was: how long would it take to adequately train a device on an individual, and which machine-learning approaches are most useful in generating accurate predictions into the future? Although random forest was the best-performing algorithm in previous work, it was among the worst-performing algorithms for forecasting the participant's mental workload in this study. Although the RMSE dropped consistently as more training data were added, the R-squared value for the testing set was negative in all cases, implying that simply using the participant's most commonly reported mental effort state performed better as a predictor than the random forest model. This meant that although random forest may have worked well for interpolation between time points, it lacked efficacy for forecasting due to overfitting. Within the scope of traditional machine-learning approaches, logistic regression actually performed better, showing low, but nonetheless positive, R-squared values, indicating that it tended to perform better in forecasting situations than simply using the most commonly reported mental effort level as the sole predictor. The statistical significance of the physiological and activity measures in the logistic regression model (Table 2) also testified to this. Within the scope of models that account for time dependency, the two-state Markov-switching dynamic regression model performed relatively well as a baseline model, with the first 30% of the data being sufficient to train the model to forecast the remainder of the data, with an RMSE around 1 and an R-squared value above 0.7. This model was simple to implement and carried interpretability, but the deep-learning algorithms showed better performance. With the first 30% of data used for training, LSTM converged to an R-squared of above 0.99, and was able to model all transitions remaining in the data. The absence of short-term memory in the RNN meant that it took slightly longer to train, with an extra 10% of training data needed to reach the level of performance of the LSTM. As seen in Figures 1 and 3, the reliance on previous observations in making predictions caused them