Justificación e importancia del estudio.

The cognitive load theory recommends to keep the amount of workload in an optimal range while presenting and studying new learning material (in this study 0.8 < Q < 3.5), to enable a successful learning support. Therefore, a workload prediction for each subject was calculated, using the calibrated linear ridge regression model as stated in section 11.3. Frequencies ranging form 4 Hz − 30 Hz were used as features. In the following section, the workload prediction results are stated for the experimental group, as well as for the control group. For the experimental group, the predicted workload results were calculated in real-time and used for the online adaptation of the presented learning material. For the control group, the predicted workload results were calculated offline for evaluation and comparison reasons, but not for adapting the presented learning material.

11.7.1 Workload prediction results of the experimental group

In Figure 11.9, the actual task difficulty and the predicted performance during the online experiment is shown for each subject of the experimental group. Furthermore, the work- load prediction based on 16 channels is compared to the workload prediction based on 15 channels, to analyze how strongly the broken electrode POz influenced the prediction results in the online study. Therefore, an additional offline analysis was conducted, where the electrode POz was excluded in order to determine its impact on the workload prediction and thus the online adaptation of the learning material.

11.7.1.1 Online workload prediction with 16 channels

During the learning session, the amount of workload was held in the predefined optimal state over time for all 13 subjects. Thus, the predicted workload for each task ranged mainly from 0.8 < Q < 3.5. But taking the observations of the neurophysiological fea- tures and the performance results into account (see section 11.5 and 11.6), especially for subject 4 and 11, it is debatable whether this workload prediction is valid, or due to tech- nical artifacts based on the broken electrode POz. Therefore, an additional offline analysis was performed, where the electrode POz was excluded in order to determine its impact on the workload prediction and thus on the online adaptation of the learning material.

11.7 Workload prediction results

Figure 11.9:Workload prediction performance during the online EEG-adaptive study with 16 chan- nels (blue line) and during an offline simulation with 15 channels, excluding electrode POz (light blue line). For better visualization, the predicted workload curves were smoothed. Furthermore, the presented task difficulty (red line) and the corresponding target Q (pink line) are shown. Par- ticipants are enumerated from top left to bottom right line by line.

11.7.1.2 Offline workload prediction with 15 channels

For the offline workload prediction, the regression model was the same as for the online experiment, but with 15 channels (FPz, AFz, F3, Fz, FC3, FCz, FC4, C3, Cz, C4, CPz, P3, Pz, P4, Oz), omitting channel POz.

As the global deviation (GD) is a good metric for capturing prediction bias errors, it was calculated to analyze the difference of workload prediction based on 15 and 16 channels (see Table 11.3). The smaller the GD-value, the smaller the predicted bias error. For subjects 1, 3, 4, 5, 6, 8, 10 and 11 a GD > 3.5 is measurable. As subjects 4, 5, 6, 10 and 11 exhibit high regression weights for channel 16 (see Figure 11.3), it can be assumed, that the learning material adaptation during the online learning session was negatively influenced by the broken electrode POz. Particularly for subject 10 and 11 a big difference in workload prediction can be recognized when using 15 versus 16 channels (see Figure 11.9). The GDreached 9.52 for subject 10 and 10.1 for subject 11. For both subjects, the workload prediction results were shifted to a higher Q-value in the offline simulation, excluding electrode POz. This might be the reason for low performance results of both subjects (see Table 11.1) based on underrated workload leading to erroneous adaptation of the learning material.

11 Online workload detection in an adaptive learning environment

Table 11.3:Performance results of the cross-subject workload prediction utilizing the RMSE and the GD between the predicted Q using 16 channels and excluding the electrode POz for each trial and subject of the experimental group.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13

RMSE 3.37 1.25 3.73 4.38 2.84 2.13 1.31 2.73 1.03 3.23 3.47 1.19 1.08

GD 9.39 0.964 11.6 18.2 6.13 3.67 0.153 5.85 0.0214 9.52 10.1 0.503 0.0592

For subjects 4, 5 and 6 the prediction bias were highest in the first trials (see Figure 11.9). As for subject 10 and 11, excluding electrode POz led to a higher workload prediction. Again, the actual workload at the beginning of the online learning phase seems to be under- rated, which might be the reason for the linearly increasing difficulty. Negative Q-values for workload prediction can be noticed in Figure 11.9 for subjects 3, 4, 5, 8, 10 and 11 during the online learning phase. For subjects 3, 4, 5 and 8, negative Q-values occurred using 15, as well as 16 electrodes for workload prediction. Possibly these values indicate subjects being disengaged due to the task being too difficult or too easy.

11.7.2 Offline workload prediction results of the control group

For the control group, the offline workload prediction was calculated based on the same re- gression model as for the experimental group in the online setup (see section 11.3). Merely nine electrodes (FC3, FCz, FC4, C3, Cz, C4, P3, Pz, P4) were used as features.

In Figure 11.10, the distribution of the actual task difficulty and the offline predicted work- load for cross-subject regression is shown. For subject 6 no Q-values were saved, due to technical problems. For 8 out of 12 subjects (subjects 1, 2, 3, 5, 8, 10, 11 and 12) the amount of workload was held in a constant range over time. For subjects 1, 3, 8, 10 and 11, the predicted workload range was 0.8 < Q < 3.5. These participants were kept in the predefined optimal workload state during the whole learning phase. The subjects 2, 5 and 12 needed a constant amount of workload, whereas the workload range for the remaining subjects 4, 7 and 9 was highly distributed and ranged in 0 ≤ Q < 5.5. For subjects 7 and 9 a decrease of predicted workload was recognized over time. This observation led to the assumption, that both subjects were overwhelmed (Q > 3.5) at the beginning of the learn- ing phase and after more than 100 trials, they reached a good workload state for learning. For subject 4 an increase of the predicted workload correlated with increasing task diffi- culty. This subject seemed to exceed the predefined perfect workload range for learning after solving more than 100 trials.