# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 5 Results and Discussion
### Identifying Target Sessions
#### 5.1.1 Targeting the Most Impactful Session  
The mt value for each session was calculated on each \(T_{t}\) for the previous three academic years, as per the process outlined in Section 3, with the results detailed in Table 2-4. Some sessions were automatically eliminated, consistent with Lemma 1 in [16], as both their corresponding support and confidence were less than other sessions in the same year. As noted in Lemma 1 in [16] and adapted for this study, if both \(\text{supp}\left(J_{i},O\right)\) and \(\text{conf}\left(J_{i}\to O\right)\) is less than \(\text{supp}\left(J_{k},O\right)\) and \(\text{conf}\left(J_{k}\to O\right)\) respectively, then \(\left(J_{k},O\right)\) is the better choice, and \(\left(J_{i},O\right)\) can thus be eliminated. In Table 2 for the 2017/18 academic year, Fri-AM had both higher support and confidence than every other session except Mon-AM, hence there was no need to compute the mt value for all other sessions except Fri-AM and Mon-AM. The mt model in Equation (3) was used to decide the better target session between Fri-AM and Mon-AM, with a minsup value of 0.550 (the lower support between Fri-AM and Mon-AM) being used. Mon-AM had the lower mt value and hence was selected to be the best session to target. The negative value for mt was also interesting to note. In practical terms, it implied that there were more records in \(T_{t}\) that contained both Mon-AM and \(O\) that were below the required levels than records that contained Fri-AM being below the required level. Thus any initiative to resolve absenteeism on Fri-AM will always be less impactful than absenteeism on Mon-AM. Hence all other sessions except Mon-AM were considered to be rare rules as there exists a \(\left(J_{i},O\right)\) combination that is under consideration with \(\text{P}(J_{i},O)>\text{minsup}\). This is not always the case and the scenarios were quite different for the 2015/16 and 2016/17 academic years. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Session (\(\text{J}_{i}\))** & **2015/16** & **2016/17** & **2017/18** \\ \hline Mon-AM & 93.7\% & 93.7\% & 93.8\% \\ Tues-AM & 94.1\% & 94.9\% & 94.6\% \\ Wed-AM & 95.1\% & 95.0\% & 95.3\% \\ Thur-AM & 94.9\% & 95.3\% & 94.8\% \\ Fri-AM & 94.6\% & 94.7\% & 94.1\% \\ \hline Mon-PM & 94.1\% & 94.5\% & 94.1\% \\ Tues-PM & 94.7\% & 95.6\% & 95.1\% \\ Wed-PM & 95.5\% & 95.6\% & 95.8\% \\ Thur-PM & 95.5\% & 95.7\% & 95.4\% \\ Fri-PM & 94.9\% & 94.9\% & 94.5\% \\ \hline Average AM & 94.5\% & 94.8\% & 94.5\% \\ Average PM & 95.0\% & 95.3\% & 95.1\% \\ Overall \((O)\) & 94.7\% & 95.0\% & 94.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average attendance for 2015/16, 2016/17, and 2017/18. From Table 3 for the 2016/17 academic year, all rules except Mon-AM, Mon-PM, and Fri-PM were shortlisted as the others were determined to be rare. The mt values were computed for each of the shortlisted sessions, with minsup set at 0.549 (the lowest support between Fri-PM, Mon-AM, and Mon-PM). Mon-AM was found to be the most impactful session to overall below-average attendance. Similarly from Table 4 for the 2015/16 academic year, Mon-AM was found to be the most impactful session with minsup set at 0.589. Given that Monday AM was found to be the most frequent and the most impactful session in the three academic years analysed, it can be concluded that the poor attendance on Monday AM may be attributed to a combination of school refusal (e.g., due to separation anxiety), and school withdrawal/truancy where the return to school may not be seen as being as exciting as the weekend that just passed [1]. Therefore, an easier, more exciting start to the school week (initiated by the school) may prove successful in addressing this issue.