The final pre-processing step employed is the Filter Stopwords (Dictionary) operator. This is done to remove all the tokens equal to the stopwords from the given text file and can be accomplished by listing down words that are assumed to be unnecessary. Another option is to download text file or document file containing pre-identified English stopwords via Internet. Take note that if the document is not in text file format, then it must be converted into that format because it is the must-be-done requirement of RapidMiner9.7. The latter option was employed and verified the content just in case that there are words that needed to be included or removed. In this paper, adjectives and some pronouns (such as he and she) were not included in the list of stopwords.

 

Table 2.

The Before and After Using Translation and Data Preprocessing

Process	Before	After
Translation	She has good teaching skills.	She has good teaching skills. I understand what she teaches. Sometimes busy that's why we can't meet but it's okay.
Transform Cases	She has good teaching skills. I understand what she teaches. Sometimes busy that's why we can't meet but it's okay.	she has good teaching skills. i understand what she teaches. sometimes busy that's why we can't meet but it's okay.
Tokenization	she has good teaching skills. i understand what she teaches. sometimes busy that's why we can't meet but it's okay.	she has good teaching skills i understand what she teaches sometimes busy that’s why we cant meet but its okay
Stopwords (Dictionary)	she has good teaching skills i understand what she teaches sometimes busy that’s why we cant meet but its okay	she good teaching skills understand she teaches busy cant meet okay

The tokenization, case transformation, and filtering stop words were done using the RapidMiner .

 

Vector creation

The schema used in vector creation was TF-IDF or Term Frequency – Inverse Document Frequency. This schema is a numerical statistic which shows the importance of a word is to a document in a corpus and considered as the most popular weight schema. In this schema, the TF-IDF value is directly proportional to the incidence of documents in the corpus that contain the word or term. Hence, as the word or term occurs most often, the value increases.  The TF-IDF will choose those words that weighted a greater importance based frothe dataset. The TF-IDF value increases proportionally to the number of times a term appears in the document, but is offset by the frequency of the word in the corpus. It helps to control or adjust because some terms are generally more common than others.

 

Pattern recognition

The method that was applied in determining the pattern recognition of comments was the Association Rule Mining. In Association Rule Mining, all itemsets must meet the set value for minimum threshold for support and confidence to arrive at a strong relationship between or among items. The formula for computing the support and confidence are given below:

SUPPORT = occurrences of [A, B] /   total number of transactions           (1)

CONFIDENCE [A,B] = occurrences of [A,B] / total number of [A]         (2)

Frequent item sets are quantified by support which is defined as the ratio between the number of instances where [A, B] appeared together in a single transaction and the total number of transactions. On the other hand, confidence is the probability of finding [item A, item B] together.

This paper used two-step process in analyzing the comments. The first step includes conducting comment word analysis using FP – Growth in terms of frequent patterns of item sets. The value set for the minimum frequency for overall data (i.e., compiled data of every campus) is different for the per campus data. The value set for the overall data is 67 and for per campus differs depending on the data set. The second step includes the examination and determination of the strong relationship between a pair of words using the Create Association Rules. The criterion used is the confidence and the threshold value set for confidence is 0.90.

 

Flow chart

The figure above shows the process that was employed from data gathering up until the use of RapidMiner to arrive at the desired results.

FP – Growth has two basic working modes in identifying the most frequent itemset/s: (1) Looking for the least specified number of itemsets with highest support without considering the 'minimum support', and (2) looking for every itemsets with a support larger than the specified minimum support. This approach uses the FP-Tree algorithm which encodes the data set into a tree and then extracts the frequent itemsets from this tree. Basically, frequent itemsets are groups of items that often appear together in the data. Using the Rapid miner 9.7, the dataset were generated and calculated based on FP - Growth method. The most frequent items and itemsets were displayed in table 5.

Table 5. Result After Using FP - Growth for the Overall Data Set

The most frequent itemsets occurred are the following: itemset good and teaching for size 2 with a support equal to 0.141; itemset good, teaching, and she for size 3 with a support equal to 0.031; and itemset good, teaching, she, and understand for size 4 with support equal to 0.005. For example, the terms good and teaching are the most common terms used in the comments on the performance of the faculty members, that is, this itemset occurs 1,410 times for every 10,000 comments. Another, the itemset containing terms explain, way, things, and difficult means that it occurs 40 times for every 10,000 comments. The results in this section is useful in considering the rules that are worthy for further analysis. The FP -Growth results per campus are provided below:

 

Table 7. Result After Using FP

 

Pattern recognition by creating association rules

Create Association Rules that is based on market basket analysis was used for pattern recognition. This operator generates a set of association rules from the given set of frequent item sets. This technique is often applied in supermarket and other shopping industries to identify the trending items which are purchased together, thus, contributing to the possible increase in profit of the said businesses by determining the items that are subject for optimization or mass production. When this method is applied to text mining, each term is considered as items and the text field is the transactions. Hence, the goal is to further identify the terms that occur together that will give us the overview on the perspective of the students towards their instructors. Applying the create association rules operator gives a clear view and understanding on the terms that have strong relationship with each other and are commonly used together by the students in giving comments or evaluation to the faculty members. The complete result for the overall data set.

A total of 13 rules were derived where in each numerical value for confidence exceeds the set threshold value (minimum confidence is 0.90) after applying the Create Association Rules operator under the Rapid Miner. This implies that the terms included in Table 4.11 have strong relationship within each other. For example, the terms way and difficult are strongly related with the terms explain and simple with support equal to 0.004373834 and a confidence equal to 0.944444444. Also, if way and difficult (premise) were included in the comment, then there is a high possibility that the terms explain and simple (conclusion) will also appear. It is not appropriate to say that when explain and simple (conclusion) occurred in the comment because of the existence of way and difficult in the same comment. Take note that Association Rules are not consequential instead they are co-existential, that is, when the premise occurred, then the conclusion will also occur at the same time. This is also due to the fact that the result has low support value but with high confidence value. Further, this implies that the rules might not stand out in the data set but it occurred frequently due to continued analysis. The same explanation goes for the rest of the rules included in the list.

Conducting the same Association Rule process per campus gives us the idea on the usual characteristics of its faculty members based on how the students perceived them. For Faculty of Education, if lessons and explaining (premise) were included in the comment, then there is a high possibility that the term good (conclusion) will also appear, hence most of the students perceived their instructors are good in explaining the lessons and critiqued them based on their teaching strategies. Also, if the term loud were in the comment, then there is a high possibility that the term voice will follow. This implies that students thought that their instructors are loud-voiced that may indicate whether resounding and clear (positive) or piercing (negative). For Faculty of Education majority of the students gave critic to their instructors through their communication and social skills due to the fact that these terms have strong relationship within each other. Faculty of Education is best known for its teacher education program having been granted by King Saud University in order to maintain the excellence and quality teaching. For Faculty of Arts, the students perceived their instructors as good in discussing topic. This might imply that students are satisfied with the teaching methods employed, and the knowledge and pieces of information delivered by their instructors. Also, students perceived their instructors as understanding especially when it comes to the situation of their students. The only downfall is that students deemed their instructors with low voice quality, hence, it explains why they don’t like noise particularly for female instructors due to relationship between the terms she, noisy, like to don’t.

For Faculty of Engineering, if the terms good, teaching, and low appeared in the comment, then the term voice will appear at the same time. This implies that majority of students recognized their instructors as good in teaching but with low voice, especially when the instructor is female because of the strong relationship between the terms she, low and voice. Also, the terms loud and voice appeared on the result asserts the existence of instructors that possess audible voice. Just like in Faculty of Business Administration, this may indicate whether resounding and clear (positive) or piercing (negative). The ambiguity of the usage of loud and voice possibly due to the insufficiency of objects and complements in the comments of the students, thus, majority of the comments are deemed as incomplete sentences.

For Faculty of Islamic Studies, most of the students perceived that their instructors can explain difficult topics, lessons, and other similar things in a simple way due to the fact that the terms simple and way has a strong relationship, also the itemset simple, way, difficult, and explain has strong relationship. This is a good skill for any educator in ensuring the in-depth understanding and easy acquisition of knowledge and pieces of information to the students. Furthermore, this implies that the instructors have a well enough understanding on the topics and lessons they are teaching due to the fact that their students deemed that they can explain things in a simple manner.

For Faculty of Medicine, the students perceived their instructors as good in teaching just like the other campuses but abused for being kind. This might indicate that the students recognized the kindness and potential of their instructors in teaching but their kindness made them vulnerable to abuse which imposes a serious concern. One of the things that an educator must possess is the ability to assert authority inside the classroom. It is believed and through experiences that too much authority inside the classroom affects the learning of the students and impedes innovation and feedback on the part of the students that is why instructors need to set boundaries on when to show kindness and when to impose strict authority. Thus, instructors in this campus should exercise balancing between imposing authority and applying student-centered approach.

Lastly, for Faculty of Science, most of the students perceived their instructors as good in relating topics in real life situations and in-depth subject matter. This might indicate that the instructors are good in using analogy in discussions, hence, this further indicates their thorough understanding on the subject matter they are teaching. Another, students recognized their instructors whether loud-voiced or soft-voiced akin to Lingayen Campus. Also, instructors are deemed by students as late in class or tardy. Tardiness in work implies irresponsibility and unreliability. Tardiness of instructors promotes disinterest in the students, worse, truancy or absenteeism on the part of students. Instructors must ensure their attendance and punctuality in class to set a good classroom environment and to serve as an exemplar for the students.

It is meaningful and just to introduce another parameter since the support value is low with high confidence value to measure the effectiveness of the association rules - which is the lift. The lift is the ratio between the support and the product of the probabilities of the premise and the conclusion (or the left and right hand side) occurring assuming that there is no association between them or independent from each other. The lift measures the strength of the association between the premise and conclusion, hence the larger the value of the lift the stronger the association between them. A lift value equal to 1 suggest that there is no meaningful association between the premise and conclusion. Notice that every lift value of each of the following derived rules is greater than 1, thus, there is a strong association between the premise and conclusion. The top three rules with the strongest association per campus are highlighted in yellow.

 

Conclusions

The aim of this research is to develop a model to predict the performance of the faculty using associative rule based on existing evaluation form use by Higher Technological Institute to evaluate faculty members. The study aimed to:

• To determine the most frequent word/words to describe the performance of a faculty member.
• To determine the performance of faculty members of every campus of the Higher Technological Institute using the associative approach text analytics technique.

The proposed model is designed to utilize the knowledge of associative text analytics capabilities that will provide a great support for decision making of Higher Technological Institute. The result reveals that the term good is still the top one terms occurred for all campuses followed by teaching. The results indicated that teacher/faculty members in all campuses are good teachers. Associating words reveal that “teaching good subject/topic”, “explain simple” and other meaningful associated words that can be utilized to evaluate the performance of the teacher. The results exposed not only the quantitative values of faculty evaluation it also exposed the qualitative opinion of the students in the performance of their faculty members.

This study reveals important aspects of faculty member’s teaching performance in terms of words / association of words that will describe their teaching performance. The results can be utilized coaching / and mentoring by faculty members in terms of their weaknesses. The proposed model can be utilized by Higher Technological Institute to evaluate their faculty members in terms of their teaching performance utilizing the comments / opinions of the students.

 

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