## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Affective States** & **Classifier/Method** & **Key Features** & **Dataset** &
\begin{tabular}{c} **No. of** \\ **Students** \\ \end{tabular} & **Results** & **Offline** \\ \hline
2014 & \begin{tabular}{c} not engaged, \\ nominally engaged, \\ engaged \\ \end{tabular} & \begin{tabular}{c} Linear regression, \\ multinomial logistic \\ regression \\ \end{tabular} & \
\begin{tabular}{c} Only head \\ pose and eyes \\ features \\ \end{tabular} & Self-generated &
\begin{tabular}{c} 34 \\ (9 male, \\ 25 female) \\ \end{tabular} & F-score: 0.369 & \(\times\) \\ \hline
2015 & \begin{tabular}{c} low, medium, and \\ high attention levels \\ \end{tabular} & SVM & \
\begin{tabular}{c} Head \\ movement \\ patterns \\ \end{tabular} & Self-generated &
\begin{tabular}{c} 35\% female \& \\ 65\% male \\ \end{tabular} & ACC: 0.89 & \(\surd\) \\ \hline
2017 & \begin{tabular}{c} engaged and \\ distracted \\ \end{tabular} & \begin{tabular}{c} SVM, logistic \\ regression \\ \end{tabular} & \
\begin{tabular}{c} Head pose and \\ eye gaze \\ \end{tabular} & Self-generated &
\begin{tabular}{c} 10 (3 male, \\ 7 female) \\ \end{tabular} & ACC: 90\% & \(\times\) \\ \hline
2019 & \begin{tabular}{c} not engaged, \\ normally engaged, \\ and highly-engaged \\ \end{tabular} & CNN & \begin{tabular}{c} Facial Action \\ Unit \\ \end{tabular} & \
\begin{tabular}{c} DAiSE \\ Dataset \\ [46] \\ \end{tabular} &
\begin{tabular}{c} 112 (32 females \\ and 80 males) \\ \end{tabular} & ACC: 89\% & \(\times\) \\ \hline
2020 & \
\begin{tabular}{c} engaged, \\ non-engaged, neutral \\ \end{tabular} & Inception v3 &
\begin{tabular}{c} Facial \\ expressions, \\ hand gestures, \\ and body \\ postures \\ \end{tabular} & Self-generated & 50 & ACC: 86\% & \(\times\) \\ \hline
2021 & \begin{tabular}{c} low level, \\ high-level \\ engagement \\ \end{tabular} & \begin{tabular}{c} LSTM and TCN, \\ fully-connected \\ neural network, \\ SVM, and RF \\ \end{tabular} & \begin{tabular}{c} Eye movement, \\ gaze direction, \\ and head pose \\ \end{tabular} & \
\begin{tabular}{c} DAiSE \\ [46] and \\ EmotionW [47] \\ \end{tabular} &
\begin{tabular}{c} 112 (32 females \\ and 80 males) \\ \end{tabular} & ACC: 63\% & \(\times\) \\ \hline
2021 & \begin{tabular}{c} completely \\ disengaged, barely \\ engaged, engaged, \\ and highly engaged \\ \end{tabular} & Neural Turing & \begin{tabular}{c} Eye-gaze \\ features, FAU, \\ head pose, and \\ body pose \\ \end{tabular} & \
\begin{tabular}{c} DAiSE \\ [46] \\ \end{tabular} &
\begin{tabular}{c} 112 (32 females \\ and 80 males) \\ \end{tabular} & ACC: 61\% & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of affective states-related work. These studies concluded that it does not directly impact but rather indirectly affects learning approaches [50]. Hence, we may say that SE is impacted by chronotype, but it has not been detected through emotional and behavioral patterns in real classroom environments. The inadequate student affective state computation may lead to constrained insights. This may be misleading for the demographic analysis. Hence, it may become a hindrance to decision making regarding student learning. The effective computation of SE in a large offline classroom environment requires the attention of the research community. Moreover, the impact of timestamps and gender on students' affective states is yet to be explored. Hence, this work aims to assist decision making regarding student learning in educational institutions. In addition to these matters, some students need a particular environment to study which can also have an impact on their engagement, but different methodologies can be used to enhance their learning process [51; 52], but our proposed work considers all ordinary students. Although the proposals are nuanced, the presented work overcomes several limitations discussed in the aforementioned section of objective computation of SE and subsequent gender-wise and timestamp analysis.