## 4 Discussion and Future Directions
### Answering Research Question Two
This is a problem when designing models that fit real-world use. Since the end users of the models developed in this study are educators, it is crucial to develop cost-effective models that perform well. The above type of models can be designed utilizing the theories of cost-sensitive machine learning [50]; however, these have not yet been utilized in existing works till the present day, to the best of the authors' knowledge. Research gap 03--Limited diversified datasets comprising EEG recordings of learners engaged in diversified learning activities: It was revealed that till the present, there are only four datasets that consist of EEG data samples related to learners' confusion levels. Of the four datasets, the only dataset publicly available is [PERSON] et al. 's [5] dataset. This dataset comprises 12,811 data samples that are utilized for model training and testing in existing works that have built models using various ML and DL algorithms. This dataset has its own issues that are identified as follows: (i) It comprises data extracted from a well-defined sample: a group of ten college students whose subject majorswere not specified. They were asked to learn from MOOC videos with topics; however, they failed to specifically mention the subjects covered in the videos. They stated that their audience would be confused; however, they did not justify the confusion induction stimulus. When a well-defined population is used to extract training data, ML and DL algorithms are prone to overfit, and the generalizability of the model is influenced by the EEG recordings of the human subjects who participated in the experiment [49]; (ii) Metadata on demographics of the participants such as age group, gender, ethnicity, and psychological factors during the time of the experiment are not reported. Metadata provides complete and detailed information about the population on which the algorithms were trained, tested, and validated. This is crucial for assuming that algorithm performance can be extrapolated to different populations [49]. Also, integrating metadata into algorithm development would have implications for increased classification accuracy as it would closely reflect the practice of an educator who analyzes students not only through cognitive perspectives but also through demographics [49]. The unavailability of another dataset similar to that of [5] leads to a problem, whereby the developed models cannot be tested for their levels of generalizability, which requires new and unseen data. Research gap 04--Existing confusion emotion models were not tested on new, previously unseen data: It was revealed that none of the research works reported evaluating the trained model performance within a real-world population. This could be due to: (i) unavailability of datasets other than the dataset used for model development or (ii) resource unavailability for the authors to test the developed system on new and unseen data. The trained models were evaluated against the data from the dataset itself. The majority of the evaluation utilized either a 5-fold or 10-fold evaluation technique to predict the model's ability to predict given new instances. Although this approach tests the model's prediction performance, which was trained on a particular complexity, it does not test the performance of the model if it is given data having different complexities than the one trained on (e.g., data consisting of similar patterns but not the same as those of the training dataset) [9]. Due to this, it is questionable whether the trained models would have good performance if given independent datasets.