Teaching is considered to play a vital role as the formal medium for learning competence on the part of students[1]. Considering that effective teaching can facilitate learning, teaching is critical in the formation of knowledge, skills, and attitude. Low-quality teaching could result in unsuccessful learning. In fact, according to data in the Teaching Perspective Inventory by Pratt and Collins [2], over 90% of teachers hold only one or two perspectives as their dominant view in teaching and teachers’ perspectives vary with their views of knowledge, learning, and teaching. Most instructors, moreover, use a “one-size-fits-all” approach when teaching [3]. This approach could be misguided when applied to teaching practices. The one-size-fits-all approach is also not appropriate because individual students are naturally different from others in their cognitive and affective characteristics. Related to the affective factor, students may have different levels of motivation, different attitudes toward teaching and learning, and different responses to a specific classroom environments and instructional practices [3]. In terms of learning English, for example, some students are highly motivated because they want to work in an international company while some of them are even not willing to learn it.
The concept of affective factors described by Ellis [4] and Brown [5] clearly outlines the importance of affective factors in the learning of a language. These factors include, among others, motivation, attitude, inhibition, and anxiety. Notably, Williams and Burden [6] also emphasize the importance of student psychological factors for teachers to realize in language learning. More importantly, on a highly conceptual level, Immordino-Yang and Damasio [7] also argue the relevance of affective and social factors in learning. They put affective factors, rather than cognitive factors, as having a primary role in student learning. They argue that aspects of cognition are affected by and placed under the processes of emotion, which they term emotional thought. Affective factors play a role as a “rudder” that guides student rationality in mobilizing their cognitive potential to result in more rational actions. Attempts thus far to systematically maximize the benefits of affective factors in education have not unfortunately been entirely successful.
Filling in the gap between teaching perspectives and student diversity by examining and estimating student ability in affective factors could be a guide to teaching effectiveness. Estimating student ability can be utilized to reveal the strengths and weaknesses of a particular student. As has been suggested by Felder and Brent [3] in areas of learning, understanding student differences is a process of “characterizing students” in their affective factors. Identifying student characteristics in advance is a benefit for the instructor in a number of important ways, such as selecting teaching materials, applying appropriate teaching methods and strategies, and determining learning resources and teaching media best suited for students.
This study presents a neural network model for estimating the English ability of students based on affective factors in learning. Related work is presented first, followed by a brief overview of affective factors, and the three major factors of motivation, attitude, and personality are defined for application in our study. Data will be collected on student affective factors and the reduction of dimensions of data by factor analysis will be described and the estimation model will be proposed. Finally, our experiment results and conclusion, including directions for future work, will be presented.
Related Work
Research dealing with the social sciences in studying student data has been performed by a number of researchers. In the field of affective factors in language learning, an attempt to reveal which type of motivation will have an influence on specific motivations and other factors that can increase motivation has been carried out by Obeidat [8]. Wei [9] also studied the interrelatedness between motivation and anxiety. Halpern [10] investigated linguistic, cognitive, and affective factors that impact on English language learners’ performance in a reading tests. The results of research by these researchers give an insight into the important aspects underlying students’ internal factors and the interrelatedness among individual factors in language learning.
Advanced research in predicting student performance using algorithms also has been carried out. Kumar et al. [11] applied data mining using k-means clustering to group data having the same features, and using decision trees for pattern analysis. A prediction method has also been reported using Neural Networks (NN) to classify students’ graduation outcomes at a 2-year institution, inferring which students would graduate successfully [12]. In this study, 12 parameters in student profiles were used to predict graduation outcomes. A series of tests and experiments were performed to get the best average prediction rate of 77% on test data. Based on a study by Ibrahim and Rusli [13], NNs also outperform decision trees and linear regression in predicting students’ academic performance.
NNs have been widely used in areas of prediction. The wide range of applications of NNs in many fields and sectors is due to their power to model behavior to produce an approximation of given output [14]. Our study has also been motivated by the potential use of NNs in yielding estimations for producing output based on affective factors that can be used to predict actual English learning outcome.
Affective Factors
Bloom [15] classifies human learning potential that can be explored within an education contexts into cognitive, affective, and psychomotor domains. These three domains ideally are considered when a study on their roles is set to examine one student’s success in learning.
In the area of language learning, affective factors are factors that are related to learners’ emotional states and their attitudes toward the target language. In affective issues there has been extensive research in which most researchers have taken the common basic understanding that affective factors play an important role in language learning. These researchers, however, have different opinions about the factors underlying affective factors. Brown [5], for example, holds the view that affective factors are those factors that come from the learners themselves. Ellis [4] meanwhile has different ideas on affective factors in second language acquisition. Ellis says that these affective factors are influenced by personality factors, such as anxiety and that how anxiety affects learning depends on learning conditions. Classification by researchers generally falls into the following: self-esteem, inhibition, risk taking, anxiety, empathy, extroversion, and motivation.
Based on the various classifications of the affective factors described previously, we propose three major factors to be put under the general heading of “affective factors.” These are motivation, attitude, and personality. The components of each of these factors are further identified by exploring each factor conceptually. The motivation factor can be divided into integrative, instrumental, resultative, intrinsic, global, situational, and task-based. The attitude factor can be divided into attitudes toward the community, English, and learning. Personality factor can be divided into introversion, extroversion, anxiety, self-esteem, and inhibition. The features of these motivation, attitude, and personality factors are shown in Tables 1–3, respectively. In this study, these three major factors and their corresponding components are utilized as the basis for exploring student success in learning English. The choice of affective factors as main factors is based on the work by Immordino-Yang and Damasio [7] defining the primary role of affective factors in learning.
English Abilities Estimation Model Data Collection on Students’ Affective Factors
A questionnaire will be used to quantify students’ affective factors as knowledge suitable for NN input. The questionnaire was developed using Likert’s five-level response scale from 5, indicating strong agreement to 1, indicating strong disagreement. We have summarized and arranged these into the three major factors mentioned in the previous section, i.e., motivation, attitude, and personality. Each factor has sub-factors reflected in motivation, attitude, and personality, as shown in Fig. 1. The questionnaire is a modified version of questions in the instruments developed by Horwitz et al. [16].
The objective of this questionnaire validation by an English expert is to check and confirm that the questionnaire was appropriately constructed in terms of content accuracy and language expression. The next step taken on the developed questionnaire is to check it for the internal consistency of each question. Reliability analysis of the questionnaire using coefficient alpha will be employed to measure the internal consistency of psychometric scores for sample examinees. Our data attributed depanding on survey such as:
Nationality: Bosnian-1 , Turkish-2
Grade( last school): Excellent-5, Very Good-4, Good-3, Regular-2, Poor-1
Knowing languages: 3 (with native)
Prior to checking the internal consistency of the questionnaire, trial data will be gathered from each student. Regarding the ability of students in learning English, we also can obtain individual English ability scores for general profiency, listening, reading, speaking, and writing. Our desired(target) outputs are five.
Subjects
Subjects responding to the questionnaire will be nearly 150 students of All Department of Freshment Classes in two universities in Bosnia and Herzegovina. The subjects’ major will be English.
Model Estimation by NN
A three-layered ( Input Layer, Hidden Layer, Output Layer) multilayer perceptron is developed to estimate student ability. We can construct five network models to estimate each English general profiency, listening, reading, speaking, and writing. Input Layer of each network, consists of 54 neurons for each question features based on the affective level. The optimum number of neurons for hidden Layer will be find giving different number of neuron. The number of hidden neurons will be chosen heuristically according to the least error during the training of the data set. The output layer, consists of 5 neurons denoting the inferred ability of a student in a given English skill, i.e., general, listening, reading, speaking, or writing.
Conclusion
In this study, we can estimate student ability in English from the perspectives of several affective factors. Doing so in the earlier possible stage could provide another perspective that would help both educators and students improve learning and teaching processes and activities. Seen from the student’s side, the recognition of weaknesses and strengths in specific ability may encourage a student to study harder to improve weaker abilities. Seen from the educator’s point of view, knowing the student’s affective levels in terms of motivation, attitude, and personality will help educators choose teaching methods and materials best suited to the student. Educators could also focus on selecting most suitable instructional media and learning resources that would best suit students.
In addition to these, utilizing assessment devices to measure students’ English learning achievement, educators may also consider the characteristics of the student. Most importantly, however, teaching and learning activities in the classroom that take into account of the student’s affective characteristics potentially will make classes more interesting and favorable while reducing stress on students taking classes. All these are expected to end up with students’ optimum learning outcome.
For this purpose, a new approach based on Neural network models constructed by a three-layered perceptron will be trained using a Back Probagation (BP) algorithm to learn the problem presented. The accuracy of the models to infer individual student ability in English will show scores in five skills.
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