Application of Predictive Model for Elementary Students with Special Needs in New Era University

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1 Application of Predictive Model for Elementary Students with Special Needs in New Era University Jannelle ds. Ligao, Calvin Jon A. Lingat, Kristine Nicole P. Chiu, Cym Quiambao, Laurice Anne A. Iglesia College of Computer Studies New Era University Quezon City, Philippines Abstract This paper presents the forecasting model of grades of New Era University elementary students with special needs that predicts a student's GPA for the improvement of his/her performance in class. Specifically, the research gathers data of students in the Form-137A, and presents the result that represents the output of the study to serve as a decision-making for the students and teachers. Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. Example for this is the computation of a variable that is used as a predictor at designated future date. Prediction is similar, but more general term. The research also focuses on Data Mining, which is the way of discovering new topics on not particular research to study in a large data which involves statistic, database system and artificial intelligence. We can conclude that the goal of data mining is to process data information from a large data or set of data into understandable and organized structure for further use. Aside from data mining, the research involves database and data management aspects, data pre-processing model and inference, processing of discovered data and visualization of information. Researchers used Multiple Linear Regression for forecasting and predictions, and a decision tree (ID3) for classifying the independent and dependent variables. From the results, we can see the correlation of predictors that shows the relationship of the variables. Keywords-forecasting; data mining; education; special education I. INTRODUCTION Curiosity is a state of active interest or genuinely wanting to see more about something, create openness on unfamiliar things. Curiosity can help us nourish and enhance our everyday task into interesting and enjoyable experience. In conducting research, curiosity can sense what will happen in the present moment regardless of what would be the outcome or what might have expected to be. It can help a researcher think, explore, investigate and learn new things on a particular topic. Due to curiosity, researcher chooses prediction to nurture and develop their knowledge in a wide topic. In recent years, data mining has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making [2]. Data mining can be used in an infinite number of possibilities. Data mining allows the ability of a computer to decide the outcome of events based on the relationships between the set of data. Reading specific patterns from the set of data, data-mining makes it possible for computers to make a precise prediction close to 100% accuracy. In computer science, data mining is the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets. Data mining is usually used in business like banks, insurance, stores, or even in science like researches about medicine and government security which involves detecting criminals and terrorists [4]. Data mining is the practice of automatically probing sizably voluminous stores of data to determine patterns and trends that transcend simple analysis. Data mining uses mathematical algorithms to separate the data and estimate the probability of some events. Data mining is more about prediction, statistic, and groupings like the segment of the population that has income with a particular range. This would help every computer science student to discover and learn more not just in the field of computer but in businesses and science research. People may have different interpretation to big data. In forecasting, the decision maker needs to find different ways to gather values from big data. Also in weighing the multiple sources of data, internal and external are basis to produce good and best result. Depending on what to make, when to make, and for whom is a complex process. Understanding the factors that drive demand, and how these factors interact with the production processes or demand and change over time, are keys in deriving value in this context [5]. How can a computer predict or analyze data using data mining if there is no data to be processed? Big data is there in conjunction with data mining to make forecasting possible. With a large amount of relative and related data, an accurate forecast can be made. For a professional educator, it s inevitable that they are given opportunities to teach those students with special needs. This is one of the challenging roles of a teacher because students need more attention to maintain the effectiveness of their curriculum, which includes freedom in classroom like communication with the class or learn better with the structure. One of their difficult tasks is to instruct the students individually, which is based on the disability. A research 47

2 shows that students with special needs tend to learn better in a special education classroom environment rather than an integrated one. An instance of this is 11 poor readers, diagnosed with learning disabilities, showed that they've improved twice as much per week in a special education classroom. This just shows that individual specialized instruction is more efficient for students with such conditions [3]. Schools must efficaciously construct a curriculum balancing integration and time in a classroom for students with special needs while constantly balancing discipline and ingenuity. This curriculum will surely help a school to maintain the way of teaching and continue to serve a growing number of students with special needs. The researchers came up with the title A Forecasting Model of the Grades in New Era University Elementary Students with Special Needs.. This study predicted the grades of students in the year and classified them into two ranges: independent and dependent. This technique was used to provide idea for the teachers and parents to give more attention for the improvement of their students and children. Knowing immediately or in advance where a student has difficulties allows the instructor to adjust his phase to match the student's learning. Useful pieces of software will let the instructor immediately gauge each of his students. The IBM SPSS and Rapid Miner is an application tool that will be used in this study to organize data and make a prediction so users can solve problems, improve outcomes and for smarter decision. This study will need the said application to be installed by downloading through the internet connection. This would be a help for the students and children with special needs and would also be a benefit for the teachers and parents. Aside from the predicting procedure, this study will also provide the correlation in different subjects of the students in order to know the relationship of it and how it will effect on the performance of the students. This research will limit the process of prediction for the students by doing it in at least 3 students and it cannot be done by less than 2 students. The developed study depends on the data given by the Elementary Registrar and approved by the SPED Department Registrar in the Form 137-A. This research will limit the data, in which regular students in the given section will be excluded. Although, some of the students has different abilities which changes of the curriculum is edified. To make it possible, they made some strategies. To avail students prosper, revision have been made to divide the curriculum to benefit the low performers. Some of the changes were habituating instructional strategies, instructional materials utilized, and curricular content and assessment practices. So to make it possible to have fair grade according to their performances, most of the teachers give prices or give credits to those students who cooperate in class. This study would benefit the students with special needs who have unusual performance in the school. This would also help the teachers, school administrators, parents, guardians, shadow teachers and even researchers to understand and to focus on the interest, performance and skill of the students. This research will use dashboard and database to organize, fix and evaluate the input data easily and presentably which will give big help for better forecasting. II. OBJECTIVES This paper aims to provide a study which may help the students with special needs to improve their performance, enhance their skills, and to know how much attention they need. The purpose of the study is to predict the grades of the students in the present school year, identify the relationship of the subjects to each other and classify them into two ranges: independent and dependent variable. The researchers developed a forecasting model of the grades of New Era University students with special needs in order to know how well students were accomplishing tasks and studies. Specifically, the study focuses on the significant predictors to be considered in forecasting the grades of New Era University students with special needs and the correlation of the predictors in able to know the relationship between each predictor. Another is which data mining techniques and algorithms was the best to use in the study. As well as the significant difference of forecasted value and actual value. III. RELATED WORKS As stated by Cook, Pamela, et al,. in the study titled The impact of Inclusion that the independent variable was the presence or absence of a disability. All students received an inclusive education. In this school, typically, a general and special teacher co-taught in the academic class. Both teachers were available to provide help to any students in need. One variation of this approach involved the special education teacher focusing assistance only on these students who had individualized Education Programs. This variation resulted when the special educator was not able to participate in the class for the entire period. Classes were taught on a block schedule. The dependent variables were the grades received in the inclusion classes and the overall Grade Point Averages (GPAs) of the students [1]. Based on this study, the most accurate predictor of a student s achievement n school is the extent to which their family creates a home environment that encourages learning,, expresses high expectations for their child s achievement and future careers, and becomes involved in their child s education. In decision-making- families have meaningful roles in making decision about school, and are provided with the training and information they need to participate effectively. Flexibility is key so that families have many avenues for input and involvement. Schools must ensure that families of children with disabilities are included on school management teams. IV. CONCEPTUAL FRAMEWORK Figure 2 Conceptual Framework showed the sequential process of the developed study. The first step was gathering data that represents the students files needed in the study. The second was the selection where the best and the important variable and data were selected. Third was the pre-processing 48

3 The data that been gathered would be arranged by the researchers which will be used to make the information understandable to future researchers and readers. Multiple Linear Regression is an analytic procedure used when there are multiple variables included in the model. The proponents believe that there is more than one explanatory variable that will help explain or predict the response variable. Multiple Linear Regression Model: Fig. 1: Conceptual framework procedure showed the filtering and grouping of the data that was required in the study to organize the data. Fourth was the transformation that representation of the new variable and output data. Next, the Data Mining process was the most important part of the conceptual framework because it was the part where forecasting and predicting procedure was applied. After that, Evaluation was divided into three: High, Medium and Low level. It was needed to determine the level of performance of the students. Lastly, was the dashboard for the results which represent the output of the study. The feedback serves as the decision making for the benefit of the user. V. METHODOLOGY The researcher had the ideas, knowledge and information in the related literatures and studies to start the forecasting of special education students grads and have beneficial for the target students of the study. It incorporated uncommon work that launched on a standardized basis form for development of the information of an individual and society, with this a new forecasting model constructed to give interpretation to the study. Descriptive Research Design provides data about the population or universe being studied. But it can only describe the questions: who, what, when, where and how of a situation, not what caused it. Therefore, descriptive research was used when the objective was to provide a systematic description that was a factual and accurate as possible. It provides the number of times something occurs, or frequency, lends itself to statistical calculations such as determining the average number of occurrences or central tendencies. The researcher used some procedure in conducting the developed study to eliminate the difficulties that may occur in conducting the research. Researchers gathered needed data for the completion of the study which were the students profile, grades of each subjects and the total average or the GPA s of the elementary students with Special Needs. To get the softcopy of grades of the predictors the researcher request for the approval of the SPED Director and turn over the letter to the University Registrar to get the following data needed. Through correlation and clustering of the computed average represents the relationship of the independent and dependent variables to forecast using regression analysis. y i = β 0 + β 1 X 1 + β 2 X β v X v + ε (1) Where: y = an observed value of the response variable for a particular observation in the population β 0 = the constant term (equivalent to the y intercept) β j = the coefficient fot the j th explanatory variable (j = 1,2,, v) x = a value of the j th explanatory variable for a particular observation (j = 1,2,, v) ε = the residual for the particular observation in the population VI. RESULT The results of this research are the answer for the statement of the problem. So the researcher identified the significant predictors and these predictors were used to help formulate prediction. The predictors are divided into two; independent and dependent. The dependent variable represents the output or effect while independent variables represent the input or causes or the tester to see if they were the cause. Statistical models normally how one set of variables, called dependent functionally depend on another set of variables, called independent. The functional relationship does not necessarily reflect causal relationship i.e the independent does not necessarily describe cause. The predictors for this research are the grades of Grade 1 to Grade 5 per subject. All of the variables above were needed to have value for forecasting accurately. ENGLISH, MATH, EKAWP, FILIPINO, HEKASI, MSEP and COMPUTER subject of Grade 1 to Grade 4 were the response that were measured while ENGLISH, MATH, EKAWP, FILIPINO, HEKASI, MSEP and COMPUTER of Grade 5 were the predictors that changed. Figure 2 show the relationship between the variables was determined. As noticed the subject that was correlated itself has the value of 1. If Sig (2 Tailed) value was greater than.05 there was no statistically significant correlation between two variables. It signifies, increases or decreases in one variable do not significantly relate to increases or decreases in the second variable. If the sig (2 Tailed) value was less than or equal to.05, it was highly accepted or it is correlated. That signifies, increases and decreases in one variable do significantly relate to increases or decreases in the second variable. 49

4 Fig. 2. Correlation for Elementary level So when the Pearson s r results 1, it only means that there was a perfect relationship between variables but if Pearson s r results 0, it means that there was no relationship between the variables. N is the number of the cases used in correlation. For example, in the subject COMPUTER, it was correlated to each other. By looking at the Pearson s r value and Sig.2 tailed value it was very easy to identify if the particular subject had relationship between a particular subjects. The subject EKAWP with p-value.570, Sig. (2 tailed) =0.43 and HEKASI with p-value of.114, Sig (2 Tailed) =.376 were the subjects that were not correlated to Computer. Subjects MSEP with the p-value of.858, Sig (2 Tailed) =.001 and SCIENCE with p- value of.842, Sig (2 Tailed) =.001 were moderate correlated to the Computer Subject. While, ENGLISH with p-value =.913, Sig. (2 Tailed) =.000; FILIPINO with p-value=.903, Sig (2 Tailed) =.000; MATH with p-value of.947, Sig (2 Tailed) =.000 were the subject that correlated with the subject COMPUTER. In the Figure 3, the researchers tested the significant difference in the actual value between forecasted values in different subjects in Elementary level: variable used are the subject of Grade 1 to Grade 4 which are ENGLISH, MATH, SCIENCE, EKAWP, FILIPINO, MSEP, HEKASI and COMPUTER. In the table, forth column was significant (2 tailed) values associated with the actual and forecasted values in a particular subject. As we noticed the sig (2-tailed) value was lesser than 0.05, so there was a significant difference. Therefore sig(2-tailed) having a value of and with the use of MAPE which measures the accuracy of a method for constructing fitted time series values in statistics. So for having a perfect fit, MAPE was zero.a multiple regression was used to predict the grades in each subject ( Computer, EKAWP, English, Filipino, Hekasi, Mathematics, MSEP and Science) of the graded five students from the GPA of the subject (Grade 1-Grade 5). The general form of the equation to predict the grades five was: Subject_Gr5 Predicted = Constant Value + (Variable x1 * Subject_Gr1) + (Variable x2 * Subject_Gr2) + (Variable x3 * Subject_Gr3) + (Variable X4 * Subject_Gr4) (a) (a) The researcher interpretation for the formulated model presented by the Subject_Gr5 Predicted was the observed value of the response variable for a computation, Constant Value was the constant term for the particular subject (equivalent to y-intercept), Variable X1,X2,X3,X4 were the coefficient value for the explanatory variable for a particular subject and Subject_Gr1, Gr2, Gr3, Gr4 were the value or the GPA of the particular subject. Elem_gr5 Predicted = _ (-.018 * Gr1) + (.016 * Gr2) + (.639 * Gr3) + (.270 * Gr4) (b) (b) The researcher interpretation for the formulated model for the whole elementary was presented by Elem_Gr5 which was the observed value of the response variable for a computation. The value was the constant value for the model (equivalent y-intercept), while was the coefficient value for the grade 1, Gr1 was the value of the GPAs of grade 1; 0.16 was the coefficient value for the grade 2, Gr2 was the value of the GPAs of grade 2; was the coefficient value for the grade3, Gr3 was the value of the GPAs of grade 3;0.270 was the coefficient value for the grade 4, Gr4 was the value of the GPAs of grade 4; Table 2 Significant Difference in Elementary Level (overall) ELEMEN TARY Sig(2- Tailed) Mean Difference ACTUAL 0 81 FORECASTED MAPE Fig. 3. Significant Difference in Elementary Level Using T-Test In table 2, the researcher tested the significant difference o actual value between forecasted values of overall performance: Elementary Level. In the table, forth column was the significant (2-tailed) values associated with the actual and forecasted values from Elementary and High School. When sig. (2-tailed) was lesser than 0.05, there was significant difference 50

5 between. Therefore, there was a significant difference for having a value of and with using MAPE which was a measured of accuracy if a method for constructing fitted time series values in statistics. When having a perfect fit, MAPE was zero. VII. CONCLUSION AND DISCUSSION In this study, we noticed how dedicated special education teachers were to teach the students with special needs, how they show their respect, patient, willingness to teach, love and support for those who need more attention than the regular students. Nowadays, many students with special needs are given a lot of skills and talent so sometimes they cannot focus on their study. Thus, this study showed how they are good and intelligent in school regarding with their grades and performances. Consequently in forecasting, we must focus on the relationship of the independent and dependent variables to obtain a result. This research correlated subjects by subjects in order to know the relationship of it. To evaluate the output of this study, Multiple Linear Regression was used as the best data mining technique hence, this research achieved the goal prediction and had acceptable outcome. Decision Tree is also used in this study in prediction and getting ranges for the needed output but it doesn t get usually used as the Multiple Linear Regressions. Researcher recommends to those who will conduct forecasting or data mining to look for other data mining techniques to enhance the prediction of a particular topic. The future researchers may contribute other features like the prediction of those students with special needs that can send to a regular class based on the output to help the School Administrator. We therefore conclude that if the result is 1, there was a perfect relationship between the variables but if the result is 0, there was no relationship between the variables. The forecasted value signifies that the increase or decrease in one variable may relate to the increase or decrease in the second variable. REFERENCES [1] Cook, Pamela, Impact of Inclusion. ND Retrieved from ck.pdf; published [2] Godswill Chukwogozie Nsofor A Comparative Analysis of Predictive Data- Mining techniques.godwill s Thesis, August 2006 Retrieve from ; unpublished [3] Hocutt,Anne Methods of Educating Special-Needs Students, August 2011 Retrieved from [4] 4Paden, Abhinn. Abhinn Pandey Int. Journal of Engineering Research and Applications. Vol. 4, Issue 12( Part 4), December 2014, pp Retrieved from %204/K pdf [5] Rey, Timothy D, Using Data Mining in Forecasting Problems: Data Mining and Text Analysis 2013 Retrieved from ; unpublished 51