Learning Style Model Detection Based on Prior

Learning Style Model Detection Based on Prior Knowledge in E-learning System MS Hasibuan1,2 1

2

University Gadjah Mada Institute Business and Informatics Darmajaya Indonesia [email protected]

Abstract— The currently existing learning style model detection based on prior knowledge can be divided into two approaches, i.e. data-driven and literature-based approach. Both approaches are obtained by collecting data from external factors of learners. External factors are strongly affected by the behavior of learners when accessing e-learning system. On the other hand, internal factors remain unattended, e.g. prior knowledge and skills of learners. Previous researches works employed the Know Want Learn (KWL) technique to revive prior knowledge using Brainstorming and Cognitive Chart. The previous three technique are deemed being less effective and dynamic as the response remains taking a long time and highly subjective. This research proposes a method for reviving prior knowledge based on Bloom’s taxonomy. We claim this method more objective as it is derived from the way of the learners acquire their knowledge and skills. Keywords : Prior Knowledge; learning style; e-learning system

I. INTRODUCTION The objective of the two learning models, i.e. digital and conventional learning, is to provide knowledge and skills for learners. Previous researches showed that there are 3 changes we can observe from the result of learning. They are changes in cognitive, affective, and psychomotoric aspects. Cognitive change can be observed from the improved knowledge acquired by learners. This change is typically observable using Bloom’s taxonomy, which is widely utilized to map acquired level of knowledge. A number of previous researches have conducted revival of prior knowledge using the Know Want and Learn (KWL) technique [1][2], brainstorming [3][4] [1], cognitive mapping [5] [6][7] , and asessment, but they were less effective and dynamic [7]. These were believed as being less effective and less dynamic as the model for reviving prior knowledge remains conventional, i.e. using face-to-face meetings. The weakness of this conventional method is that judgment may sometimes be unobjective and tend to take a longer time to identify. This research aims at addressing the issue of effectiveness and dynamics using an algorithm that is able to directly read learners’ prior knowledge. The algorithm will read the results of learners’ evaluation and further link it to Bloom’s taxonomy. The method to adopt is Latent Semantic Indexing (LSI) method

LE Nugroho1, IP Santosa1 1

Departemen of Electrical Engineering University Gadjah mada Yogyakarta [email protected], [email protected]

[8]. The LSI method will be modified by linking it to Bloom’s taxonomy, known as the Latent Semantic Assessment (LSA). LSA is capable of reviving prior knowledge through a document assessment analysis process. This paper is divided into four sections: 1. Background, 2. State Of The Art, 3. Proposed Learning Model, 4. Discussion. II. STATE OF THE ART Prior research on prior knowledge revival processes has been performed by several researchers using brainstorming [3] [4], KWL Chart [5], cognitive map [6] [7] and assessment [8] [9] [10]. Priority revival process using Brainstorming performed by Science dan buzan need long time [4] [3]. This is because the process of brainstorming is done with face-to-face interaction between teachers and learners. The face-to-face process also results in very high subjectivity, so the end result of the brainstorming process is not very accurate. The second prior knowledge revival is by using Know, Learning and Want (KWL Chart) method [5]. This KWL process is as almost the same approach as brainstorming. The difference that this process using the KWL table will be filled by the learner. This KWL table will be filled by the learner by filling the Know, Want and Learn tables which indicate the knowledge of each learner participating in the learning process. The third one is the cognitive map which is used to map one's knowledge. The beginning of the cognitive map was introduced by Buzan in 1974 with the concept of mind maps [11]. This cognitive map is useful in the stages of defining problems, developing ideas or ideas, and designing appropriate learning. The problem of cognitive map of learners is that they must understand the symbol of cognitive map concept. After they can use they concept to solve anykind of problems. While the last prior knowledge revival is using assement technique [8] [1] [10]. This assessment technique is done by testing model such as initial knowledge survey, material review and finally post test. At this Stages, the technique requires long times. From the state of the art’s result of prior knowledge revival, it can be concluded that the main issues are time, effectives of process, and high subjectivity. So that we need a new method that is more fundamental in prior knowledge revival.

Bloom’s taxonomy is the base of the model in this reseacrch. The Bloom’s taxonomy concept was developed in 1956 by Benjamin S. Bloom. Bloom’s taxonomy is a process of classifying the thinking skills of a learner into several levels. There are 6 levels, i.e.: remembering, understanding, applying, analyzing, evaluating, and creating, as illustrated in Figure 1 below [9] . Each learner has his/her own level, and this depends on the intellectual level a learner possesses.

The research of prior knowledge revival is on based taxonomy bloom’s. Prior knowledge refers to the knowledge or skills that a learner has acquired [6]. The detection method that will be used is Latent Semantic Indexing. Latent Semantic Indexing (LSI) is a model to represent the correlation between keywords and the document being searched [11]. The research done by April et al. showed the utilization of LSI with millions documents, in which the specific document(s) being searched were found based on the keywords [12]. The research by Nur et al. also utilized LSI to find a relevant hadith among the many available hadiths by the use of keywords [13]. LSI employing query using singular value decomposition (SVD) technique was proven to be capable of finding a more effective query than the previous method [14]. In this research the assessment process uses LSI. The query definition is based on Bloom’s taxonomy, which is then classified into 4 levels of learners’ knowledge and skills. This query will represent the knowledge and skills possessed by learners. Therefore, the query can be utilized by learning agents to detect learning styles [15].

Figure 1. Process of thinking in Bloom’s taxonomy ( Illustration of scott brande: http://ezsnips.squarespace.com/blooms-taxonomy/) -

Remembering level is when learner possesses the skill of recognizing, naming and, identifying. The point of the remembering level is keywords from the learning materials. - Understanding level is when learner has already possessed the skill to define, conclude, compare, and provide interpretation of learning materials. - Applying level is when learner can use and implement the knowledge he/she has acquired. - Analyzing level is when learner has already acquired the skill to organize, structure, and integrate the knowledge and skills he/she owns. - Evaluating level is when learner possesses the skill to hypothesize, check, and criticize learning materials. - Creating level is when learner has been able to design, construct, and create. Based on the six levels of knowledge mentioned above, Chen et al conducting research mapped Bloom’s Taxonomy into 4 dimensions of knowledge, i.e.: factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge [10].

Figure 2: Mapping of Bloom’s Taxonomy and Dimensions of Knowledge [10].

III. PROPOSED METHOD Based on the discussion above, an assessment model is proposed, which further will be capable of revealing the knowledge and skills the learners own. This is illustrated in Figure 3 below.

Figure 3. Identification Concept From Figure 3, it can be elaborated that learners attend assessment process conducted by the e-learning system. Furthemore, there will be an algorithm formulation to read the results of the assessment. The formulated algorithm adopts Latent Semantic Indexing algorithm and Bloom’s Taxonomy. The assessment results will represent the prior knowledge own by learners. The process of Bloom’s taxonomy mapping and prior knowledge is presented in Figure 4 below.

IV. RESULT AND DISCUSSION The model implementation, table 1 below, learners will get 4 questions representing from: P1 = Experince P2 = Knowledge P3 = Skills P4 = Expertise Table 2 below is the key word of the answer to the question.TABLE 2 LEARNERS’ RESPONSES Figure 4. Mapping of Bloom’s Taxonomy and Prior Knowledge From figure 4, it can be seen that there are mapping from bloom taxonomy into prior knowledge. The first level, remembering is mapped to experience. Secondly, understand ,is mapped into knowledge. Third, apply, is mapped to skills and the last level – analyze, evaluate and create – is mapped to expert. Figure 5 below is a proposition of the model for reviving prior knowledge based on the results of assessment performed by the system.

Figure 5. Model for Recognizing Prior Knowledge In Figure 5, it can be seen that the assessment process will result in experience, knowledge, skills, and expertise. The algorithm used to revive prior knowledge through assessment is by adopting Latent Semantic Indexing (LSI) algorithm. LSI is performed starting by defining Query from each level of Prior Knowledge using the following with rule in table1 :

Tabel 1 In table 1 it can be explained that the experince level characterizes that the student can perform identifying and mentioning. The second feature is the level of knowledge that learners perforom explaining, summarizing and grouping. The Third level of learners who have the skills that can perfom the work of doing, associating and counting. While the level cof the expert is that learners can perfom outlining and organizing.

In Table 2, Result of learner’s responses describe that P1 represents Prior Knowledge Level 1, i.e. experience, which is also the representation of Bloom’s taxonomy, i.e. remember (C1). This phase indicates that learners can only remember one or two most dominant keywords in database learning. Regarding to this research, in which the learning materials is database, the keywords and queries are also mysql. This is based on the provision that mysql is the programming language most widely used in database learning. Next, P2 represents the level of prior knowledge, which is indicated by learners who are able to provide definitions regarding to a number of important points related to database. In this research, learners own knowledge related to database. Furthermore, the definition being discussed is the definition of mysql. The correlation between responses in P1 and P2 reflects the learners’ ability to understand the materials in a systematical manner. The next level is Prior Knowledge Level 3, known as skills. This level is equivalent to Apply in Bloom’s taxonomy. At Apply level, learners can eventually make use of their knowledge regarding to keywords and definitions into skills,. The last level is that of expertise obtained from Bloom’s taxonomy, i.e. analyze, evaluate, and create. Learners’ expertise will emerge after they have been able to analyze, evaluate, and eventually create something. Implementation of prior knowledge revival model using Laten Semantic Indexing method. The input of this model comes from the essay answer of each learner. Each learner will be asked questions related to the learners' experience, knowledge, skills and expertise about the database. Each learner's answer will convert into a word matrix shown Figure 6 below. Step 1. Setting Query and Assessment Values

Step 3: Find the new document vector coordinates in this reduced 2-dimensional space. Rows P1= -0.0491 0.0722 P2= -0.5233 0.3020 P3= -0.7691 0.2265 P4= -0.3637 -0.9232 Query: -0.6554 -0.6616

Figure 6. Assessment Matrix In Figure 6, it can be elaborated that there are some words frequently emerge, known as keywords, to which a score of 1 is assigned onto each P1, P2, P3, and P4 position, which are representations of Prior Knowledge. On the other hand, Query refers to the keywords that function as the reviving value from corresponding prior knowledge. For example, the keyword of P1 is MYSQL. Meanwhile, the keyword of P2 are database and table. For P3, the keywords are query, system, and simulating. For P4, the keywords are management, generate, data mining, and datawarehouse.

Step 4 : Rank documents in decreasing order of querydocument cosine similarities. Sim (q,p)= |q *p| |q| |p| Sim (q,p1)= -0,19169733 Sim (q,p2)= 0,25444557 Sim (q,p3)= 0,47440425 Sim (q,p4)= 0,91894255

Step 2: Implementing a Rank 2 Approximation by keeping the first two columns of U and V and the first two columns and rows of S U= -0.1676 0.1644 -0.4848 -0.1734

Calculation results in: P4 > P3 > P2 > P1 This means that Prior Knowledge 4 is the representation of expertise (P4), which undoubtedly contains skills (P3), knowledge (P2), and experience (P1). V.

-0.3783 0.2322 -0.3783 0.2322 -0.3783 0.2322 -0.2252 0.0995 -0.2252 0.0995 -0.3316 -0.3060 -0.1065 -0.1065 -0.1065 -0.1065 -0.2252

-0.4055 -0.4055 -0.4055 -0.4055 0.0995

CONCLUSION

This research has succeeded in proposing a model for reviving prior knowledge using assessment of learners’ responses to test questions. The assessment of learners’ responses was performed using Latent Semantic Assessment (LSA), i.e. a modification of Latent Semantic Indexing. This method transforms responses into matrices and provides response queries based on the level of prior knowledge-- experience, knowledge, skills and expertise --. From this model testing, the data acquired showed that prior knowledge reviving is more accurate when performed using LSI as compared to brainstorming, KWLChart, Cognitive map methods and asessment. This is because LSI is able to provide rapid, precise and objective of learners’ prior knowledge. VI. REFERENCES

S= 3.4158 0

0 2.2764

-0.0491 -0.5233 -0.7691 -0.3637

0.0722 0.3020 0.2265 -0.9232

[1] [2]

V= [3] [4]

Harsono, “Peran Prior Knowledge Dalam Problem Based Learning,” Hum. Factors, pp. 1–5. T. Thi Thanh Dieu, “Trying K-W-L Strategy on Teaching Reading Comprehension to Passive Students in Vietnam,” Int. J. Lang. Linguist., vol. 3, no. 6, p. 481, 2015. T. Buzan, Use Your Head. 1974. E. Science, “Unit 1 : Using brainstorming to elicit prior knowledge : sound and musical instruments.”

[5] [6] [7]

[8] [9] [10]

[11]

[12] [13] [14] [15]

L. B. Gouveia, “A brief survey on cognitive maps as humane representations,” 1974. T. Hailikari, Assessing University Students ’. 2009. R. M. Rias and H. B. Zaman, “Understanding the role of prior knowledge in a multimedia learning application,” Australas. J. Educ. Technol., vol. 29, no. 4, pp. 537–548, 2013. B. Rosario, “Latent Semantic Indexing : An overview,” Infosys 240, pp. 1–16, 2000. J. M. Lampinen and J. D. Arnal, “A Revision of Bloom’s Taxonomy: An Overview,” Am. J. Psychol., vol. 122, no. 1, pp. 39–52, 2009. Y. H. Chen, L. Y. Deng, P. W. Chen, B. C. Kao, and C. H. Huang, “Assessment system based on scaffolding and bloom’s theory-case in ITE course,” Proc. - 16th North-East Asia Symp. Nano, Inf. Technol. Reliab. NASNIT 2011, pp. 93–99, 2011. R. E. Story and R. E. Story, “Pergamon AN EXPLANATION OF THE EFFECTIVENESS OF LATENT SEMANTIC INDEXING BY MEANS OF A BAYESIAN REGRESSION MODEL,” vol. 32, no. 3, pp. 329–344, 1996. A. Kontostathis and W. M. Pottenger, “A framework for understanding Latent Semantic Indexing ( LSI ) performance,” vol. 42, pp. 56–73, 2006. N. Nur, A. Tuan, and M. Rahim, “A Malay Hadith Translated Document Retrieval Using Parallel Latent Semantic Indexing ( LSI ),” pp. 118–123, 2016. S. Deerwester, S. T. Dumais, G. W. Furnas, and T. K. Landauer, “Indexing by Latent Semantic Analysis,” vol. 41, no. 6, pp. 391–407, 1990. M. S. Hasibuan, L. Nugroho, P. I. Santosa, and S. S. Kusumawardani, “PAPER A PROPOSED MODEL FOR DETECTING LEARNING STYLES BASED ON AGENT LEARNING A Proposed Model for Detecting Learning Styles Based on Agent Learning,” Int. J. Emerg. Technol. Learn., vol. 11, no. 10, pp. 65– 69, 2016.