## 3 Proposed Methods
### Offline Data Engineering
#### 3.3.1 Mining of Personality Patterns  
Until now, retrieving the learning interests to facilitate learning has been a challenge. In previous work [4; 41], learning interest can be represented by a person's personality. Therefore, the goal of this offline process is to discover the relationship between learning interest and personality. In this process, the user learning log and the user personalities are incorporated into a transaction table. In this work, the learning log indicates the degrees of the learning courses. That is, a transaction in this transaction table contains a user personality, a learning course, and a related degree, which can be defined as follows:  
\[<\textit{user personality},\ <\textit{course},\ \textit{degree}>>\]  
where _user personality_ is a set shown in Table 1 and _degree_ indicates the learning degrees ranging from 1 to 5. Note, the main intent for determining the personalities in Table 1 is based on [42] personality being represented by three bipolar dimensions: [41] extroversion-introversion (EI), sensing-intuition (SN), and thinking-feeling (TF), or the five senses [3]. Because the personalities are shown to be closely related to learning preferences, these personalities can further impact learning achievement. Therefore, the determined personalities in Table 1 are included in these three aspects and five senses. For example, a transaction  
\[<\)[Male, A, Capricorn, Reading, White, Winter, Orange, Pop, Romance, Cake,  
\(<\)[Nature, Window, Paint, Yes, Well Organized], [System Development, 4]\(>\)  
indicates that the degree of system development for a well-organized male with blood type A and astrological sign Capricorn, who likes reading, the color white, winter, orange, pop music, romance movies, cake, nature tours, the window seat, prefers a paint background on the desktop, and is interested in new ideas, is frequently 4 in the transaction table.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Personality**} & \multicolumn{1}{c}{**Items**} \\ \hline Sex & \{Male, Female\} \\ \hline Blood type & \{A, B, O, AB\} \\ \hline Astrological sign & \{Aries, Taurus, Gemini, Cancer, Leo, Virgo, Libra, \\ Scorpio, Sagittarius, Capricorn, Aquarius, Pisces\} \\ \hline Hobby & \{Reading, Exercise, Music, Science, Art, Vogue\} \\ \hline Preferred color & \{Brown, Grey, [PERSON], Yellow, White, Black, Blue, Pink, \\ Green, Purple\} \\ \hline Preferred season & \{Spring, Summer, Fall, Winter\} \\ \hline Preferred fruit & \{Apple, Banana, Grapes, Cherry, Coconut, Sakya, \\ Orange, Papaya, Peach, Pear, Mango, Pineapple\} \\ \hline Preferred music & \{Love, Hip Hop, Rock, Pop, Dance, Classic, Tec, \\ Korea Style\} \\ \hline Preferred movie & \{Action, Science Fiction, Romance, Horror, \\ Animation, Comedy, Documentary, War\} \\ \hline Preferred food & \{Beef Soup Noodles, Surf \& Turf, Pizza, Fired Chicken, \\ Cake, Hong Kong Style, Rice Porridge/Congee\} \\ \hline Preferred tour & \{City, Nature, Festival, Challenging, Pilgrimage\} \\ \hline Preferred seat position & \{Window, Middle, Aisle\} \\ \hline Preferred desktop background & \{Nature, Model, Animal, Paint\} \\ \hline Interested in new ideas & \{Yes, No\} \\ \hline I am & \{Optimistic, Frank, Well Organized, Accommodate\} \\ \hline \hline \end{tabular}
\end{table}
Table 1: User personality.  
After generating the transaction table, the mining algorithm is run to mine the rules, which can be defined as:  
\[\{\textit{user personality}\}\rightarrow\{\textit{course, degree}\}.\]  
For example, a rule  
\[\{\text{Male, Exercise, Yellow, Summer, Rock, Action, Aisle}\rightarrow\{\text{Programming, 3}\}\]  
indicates that a programming degree for a male liking exercise, yellow, summer, rock music, action movies, and the aisle seat is always 3. These mined rules provide support for online prediction.