A constructivist approach to teaching sentences in Indian language

T4E 2009

A Constructivist Approach to Teaching Sentences in Indian Language Abhijit R. Joshi

Sasikumar M.

Department of Information Technology D. J. Sanghvi College of Engineering Mumbai, India. [email protected]

CDAC, CBD Belapur Navi Mumbai, India. [email protected]

Abstract—The tremendous increase in the usage of internet over the past few years and the ease of availability of resources across the globe at any time leads to the advancement of e-learning. Among the various domains, language learning is an important area with high potential for e-learning. Despite the potential of Information and Communication Technology (ICT), e-learning system for language has not taken off as expected. There are several reasons. But the main reason is the inadequate attention given to the variety of differences between traditional learning scenario and an e-learning one [10]. Our approach Intelligent Environment for Learning Indian Languages (IELIL) i.e. Marathi e-guru is an attempt to reduce the gap between these two learning models. To design IELIL, the potentials of prior research frameworks in language learning like Constructivist Learning Environment (CLE), Computer Aided Language Learning (CALL) and Intelligent Tutoring system (ITS) are integrated. We have adopted the approach of a child learning its first language, where there is little emphasis on grammar and formal structure. The learning happens through continuous experimenting with regular feedback. We discuss our approach to teaching sentences in this paper. This paper reviews the language learning domain, the frameworks like CLE, CALL and ITS, our approach IELIL and discussion about the results with conclusion and future work.

system, vocabulary acquisition system and system that allows training dialogue elements [3]. But most of these systems lack the flavor of innate learning through observation [4]; just concentrate on syntax or semantic and no attention to problem of pragmatics [3], which makes language learning process using computer boring for the student. These are some potential reasons to develop an authentic system. Our idea is to design language learning system, Intelligent Environment for Learning Indian Languages (IELIL), with the flavor of the way the children learn their first language in early age. Before moving to the detail discussion about IELIL, we discuss first the three areas of prior research in language learning namely Constructivist Learning Environment (CLE), Computer Aided Language Learning (CALL) and Intelligent Tutoring System (ITS). A. Constructivist Learning Environment Constructivism emerged in reaction to the traditional education approach widely practiced in eighteenth and nineteenth century [5]. In the traditional education approach, a teacher transmits information to the students with little emphasis given on the students’ current knowledge state and without paying much attention on the practicality and significance of content and its delivery. A constructivist approach teaches students to discover their own answer and produce their own concepts and interpretations [6]. We know that to learn a language, one has to learn grammar rules also. But, the constructivist approach provides an environment where one can feel free from rigid and restrictive rules of grammar and still learn language effectively.

Keywords- Constructivist Learning Environment (CLE); Computer Aided Language Learning (CALL); Intelligent Environment for Learning Indian Languages (IELIL); Intelligent Tutoring System (ITS)

I.

OVERVIEW OF LANGUAGE LEARNING FRAMEWORKS

B. Computer Aided Language Learning In the early days of computer, drill-and-practice kind of language teaching methodology was adopted in most of the CALL systems. Since then, the advances in computer, information and communication technology and also in second language acquisition have helped the shift of interest from repetitive exercise to more communicative task [7]. Some of the important issues in language learning process are generating a problem as per students’ current knowledge state, assessing students’ progress and offering appropriate feedback whenever student makes mistakes. Current CALL systems need further improvements keeping in mind the language learning issues and education requirements. C. Intelligent Tutoring System During the late 1950s and early 1960s, with the advent of Artificial Intelligence (AI), a new type of Computer Aided Learning (CAL) system called Intelligent Tutoring System (ITS) emerged [8]. ITS typically consists of four models namely an expert model containing

Language learning using computer is an active area of research dating back many decades. The potential reasons of language learning are many. Some of them are the growing interest of the people to know the rich culture embedded in languages, the people migrated for studies, jobs, etc to get the familiar with expressions in everyday conversations and typical dialogue in native language [1]. We know that the grammar is a core part of any language learning process. Although one never learns the grammar rules of first language during the childhood, one still construct the sentence easily and effectively in first language. The reason is that, learning is about making sense of information, extracting meaning and relating information to everyday life and that learning is about understanding the world through reinterpreting knowledge [2]. There are many language learning systems available but most of them focuses on the single element of language at a time. These systems on the basis of language learning element are classified as inductive and deductive grammar acquisition

International Workshop on Technology for Education (T4E), Aug 4-6, 2009, Bangalore

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language knowledge, student model containing students’ performance data which shows his current knowledge state, a teacher model with pedagogical principles which supports in generating lessons as per students performance data and user interface model for interaction purpose. ITS technology seems particularly promising in the fields like language teaching, where a solid core of fact is actually surrounded by a more nebulous area in which subtle discriminations, personal points of view, and pragmatic factors are involved [9]. However, language learning using ITS has not taken off as expected mainly because research is primarily driven by computer scientist and doesn’t address all the different issues from other fields [10]; no standard language for representing knowledge and a set of tools to manipulate the knowledge [11]; inadequate analysis of students response and suggesting appropriate remedial measures to correct mistakes and also deciding the right time and type of intervention required when student makes mistake [1]. Till date there is no significant work done towards the integration of these framework namely CLE, CALL and ITS. IELIL is an effort towards the same which results in an environment where student can learn sentence in Marathi language. In this paper we present some of the results obtained related to sentence teaching during the ongoing research. In the next section we discuss in detail our approach to sentence teaching. II.

With this change in adverb the other constituents of sentence remain same. Now consider the third sentence where we changed ‘baageta’ to ‘maidaanaata’, with this changed in object still the other constituents remain same. Consider the sentence no. 4 where we changed masculine kartaa (the agent of action) ‘to’ to feminine ‘tee’, with this change in kartaa a change is occurred in verb. Now, instead of ‘phirato’, ‘phirate’ is used. In this way learner can see if there is a change in adverb or object in the sentence then the other constituents remain same but if there is change in kartaa then verb gets inflected. Here, verb gets inflected due to the change in gender of kartaa. By changing component one at a time we can generate the variants of sentence so that the learner can see the structural and inflectional role played by various components. Also, the system can prompt the user to change a component and produce the revised sentence. The system would check if the changes are as expected and can generate effective feedback. In the sentence teaching process the system never conveys the grammar rules to the student directly but conveys the same by generating the variants of sentence as discussed above. However to generate the correct variants of sentence, the system is required to know the grammar rules. These grammar rules are stored as domain knowledge. As verb gets inflected with the change in person, gender, number of kartaa and tense so these verb rules are stored in domain ‘rule-verb’ in the format: ruleid:person:gender:number:tense: endings. e.g. 1:Third:M:Ekvachana:Vartmana:to. This rule signifies that if the agent of action is of third person, masculine in gender and singular in number then add ending ‘to’ to the root word of verb to get the verb form of Vartamana (present) tense. Similarly, all rules take the above form for combinations of person, gender, number and tense with respective endings. To generate correct sentence, the system needs the sentence structure and inflection rules. Now we see the sentence structure representation here. We used an XML Document Type Definition (DTD) to represent the structure of Marathi sentence i.e. the position of constituent in any type of sentences. This sentence structure helps the system in generating the syntactically correct sentence templates. The sentence template in [4] caters only simple sentences with single subject and DTD is not used to generate it. But, now using the DTD we can generate any number of sentence templates for simple (single as well as multiple subjects), complex and compound type of sentences which is an extension to the sentence template in [4]. The sentence template shown in figure 1 is of simple sentence with multiple (two) subjects. In the sentence template, an element sentence has an attribute ‘no’ which indicates the specific sentence template. It has a sub element simple which indicates that it is template for simple sentence. The sub element simple has four elements namely subject twice, conjunction and predicate. Both subjects have element main_subject and noun is the sub element of both main_subject. Both main_subject nouns have two attributes, type and name. The type has a value ‘personified’ which ensures that the kartaa (the agent of action) is always personified. This avoids generation of sentence like ‘aambaa aaNi khurchee sakaaLee baageta phirataata’ (mango and chair move in the garden in

OURAPPROACH

Our approach is based on the children learning the first language where no visible attempt is placed on teaching the grammar rules. These grammar rules are induced by the learner during the learning process. In early age the child gets familiar with the words from the language and then he is introduced to sentences. During the sentence teaching he will be made aware of the different types of constituents available in the language and their role in the sentences. Similar kind of approach we incorporated in our system to teach language. Initially we make the learner familiar with the alphabets from Marathi language and then the words. To teach sentence construction, the learner needs to be familiar with the various constituents, their positional choices and the inflections for various constituents if any. For this we show carefully selected variants of different types of sentences. We change the constituent one at a time so that the learner can get the idea about the position and inflectional role played by various constituents. Consider the table I which shows our approach of teaching sentence formation. In the table I, sentence no. 1 is the base sentences and sentence no. 2, 3 and 4 are its variants. In sentence no. 1, we changed an adverb ‘sakaaLee’ to ‘dupaaree’. TABLE I. Sr. Person No.

Gender

VARIANTS OF SENTENCE Constituents of Sentence kartaa

Adverb

Object

Verb

1

M

to

sakaaLee

baageta

phirato

2

M

to

dupaaree

baageta

phirato

3

M

to

sakaaLee maidaanaata phirato

4

F

tee

sakaaLee

baageta

phirate


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morning) which is syntactically very correct but it is not legal or meaningful. The personified type values (nouns and pronouns) are stored as domain knowledge. The second attribute name takes the value ‘X’ for first main_subject noun and ‘Y’ for second main_subject noun. These values ‘X’ and ‘Y’ are used to identify the element in the sentence whose properties are used to get the property of combine subject. This new property will now influence the verb inflection which is discussed in detail subsequently. Both main_subject element noun has four sub elements namely person, gender, number and case. Using the information about these elements the system is able to retrieve the personified noun/pronoun of specified person, gender, number and case from the domain model where all nouns are stored. In the template as shown in figure 1, the sub element person, gender and number of noun of main_subject has attribute type and it has value ‘any’. Here, ‘any’ indicates that the system will select the value for person, gender and number randomly from domain model. The person domain contains the values {First, Second, Third}, the gender domain contains the values {Masculine, Feminine, Neuter} where as number domain contains the values {Singular, Plural}. The fourth sub element case has attribute type and it takes value ‘N’. It means that both nouns (the agents of action) are in nominative case so the sentence is in kartaree prayoga and verb depends on combined properties of both the agents of action. The element conjunction has attribute type and name. The type has value ‘C’ which indicates that the cumulative type of conjunction is used to join the two subjects. The information about all types of conjunction is stored in ‘domain-conjunction’. These conjunction are stored in the format: ‘type:name’. e.g. ‘C:aaNi’, ‘D: kinvha’ etc. The system selects randomly one of the ‘C’ type of conjunction from the list. The second attribute name has value ‘C1’. As in case of multiple subjects, the verb inflection depends on resultant property after combining the properties of both nouns ‘X’ and ‘Y’. Now, this resultant property is referenced through ‘C1’ and is used to carry out the verb inflection operation. The table II shows the resultant property after combining the properties of noun ‘X’ and ‘Y’. Rule 1 and 2 in table II, for example, states that when two or more nouns are connected together and if one of the nouns be of the first person, the verb must be in the first person plural. The element predicate has three sub elements adverb_time, object and verb. The adverb_time has attribute type and it has value ‘any’. This element is basically used to indicate the time when action is performed. These action times are stored in domain model. The element object has sub element main_object and noun is the sub element of main_object. The element noun has two attributes type and name as mentioned earlier. Here, the type has a value ‘place’. The information about the ‘place’ type of the noun is stored in ‘domain-noun’. Actually all types of nouns are stored in ‘domain-noun’ only. The nouns are stored in the format: ‘type:name’. E.g. ‘personified:tee’, ‘eatable:keLa’, ‘place:baaga’, ‘drinkable:dudha’ etc. This categorization of noun is helpful in retrieving the correct noun of said type. The sub

element of noun (main_object) is same as that of noun (main_subject). But, the sub element attribute takes different value. Here, the type takes value ‘third’ for person, ‘any’ for gender, ‘Ekvachana’ (singular) for number and ‘L’ (locative) for case. Using this information system selects a noun of type ‘place’ having third person, any gender, singular in number and nominative in case from ‘domain-noun’. Then the system converts this nominative case noun into locative one. Since the verb in above template represents a moving action like run, walk etc. which is generally performed at certain place hence the (main_object) noun is required to be in locative case. The element verb has an attribute ‘dependson’ and child element dhaatoo. We discussed earlier that the verb changes with change in gender/number/person of kartaa (the agent of action). As the sentence template in figure 1 contains multiple subjects so verb is not depend on any of the kartaa (the agent of action) but on the resultant property after combining the properties of the agent of action as per table II which are referenced through conjunction name here ‘C1’. So we have introduced the ‘dependson’ attribute with value ‘C1’ which is same as that the value of an attribute name of an element conjunction denoting the resultant agent of action. This ensures that the system would generate the right form of verb as per the gender, number and person of the resultant agent of action. The child element dhaatoo has an attribute type and it has a value ‘move’. Using dhaatoo, the gender, the number, the person of the resultant agent of action and tense, the system generates correct form of verb. A. Sentence Generation Process The sentence generation process which generates the legal and meaningful sentences with the help of DTD and XML sentence template is discussed below. Get the agents of action and its details: The system selects all ‘personified’ nouns/pronoun from ‘domain-noun’ and put it into a list. As person, gender and number is ‘any’ for the first agent of action, let person be ‘First’, gender be ‘Masculine’; and number be ‘Singular’ which is randomly selected from domain model. Since all nouns and pronouns are stored in nominative case only so further transformation on it is not required. Now, system searches for each personified noun/pronoun in ‘noun-details’ for noun/pronoun which is in first person, masculine in gender and singular in number. Actually the domain ‘noundetails’ consists of the agent of action along with its person, gender and number. This information is stored in the format: ‘the agent of action:person:gender:number. The search for the personified agent of action of first person, masculine in gender and plural in number results in a string ‘mee:First:M:Ekvachana’. Here in the string the first component ‘mee’ is the first personified kartaa. Now, for the second agent of action, as person, gender and number is ‘any’, let person be ‘Third’, gender be ‘Feminine’ and number be ‘Singular. If the person of first agent of action is ‘First’ or ‘Second’ then system ensures that it won’t generate the same person for the second agent of action. In this way system takes care of generating the meaningful sentences only.


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one of them radomly, let it be ‘aaNi’. Also, it extracts the value of second attribute which is ‘C1’ here. Get the adverb of time: As the attribute of Adverb_Time ‘type’ has value ‘any’ so system selects randomly one of the action time from the domain ‘adverbtime’. Let the random selection be ‘dupaaree’. Get the main object: The system selects all the nouns of type ‘place’ from ‘domain-noun’. The domain noun returns the list { maidaana, rastaa, baaga ... }. In the sentence template, the person of main_object noun is ‘third’ and number is ‘singular’ whereas the gender is ‘any’. Let the system selects the ‘feminine’ gender randomly. Now system looks in domain ‘nouns’ where all nouns are having third person and singular number and stored in the format: ‘noun:gender’. E.g. ‘aambaa:M’, ‘baaga:F’, ‘mula:N’ etc. The search for a ‘place’ type of the noun and in ‘feminine’ gender results more than one pair of ‘noun:gender’ then the system selects randomly one of them. Let it be ‘baaga:F’. The first component ‘baaga’ is now the main object. As the case is locative in the sentence template so this main object ‘baaga’ needs to be transformed into locative case. Here, the system transforms the ‘baaga’ into the locative case as ‘baageta’. 5) Get the verb: The system selects all ‘move’ type of the dhaatoo from domain. Let the list be { phira, basa, chala, kheLa, ... }. Now system selects one of them randomly, let it be phira. Since the verb depends on the resultant properties of the agent of action rather than the properties of the agents of action ‘mee’ and ‘tee’. So a right form of a verb for dhaatoo ‘phira’ is generated by adding pratyaya (endings) of resultant kartaa as per rule 1 in table II. The rule 1 gives the resultant person as first, gender as masculine and number as Anekvachana (plural). Since endings for the agent of action of first person, masculine/feminine gender and plural number are same in all tense. So, masculine gender is considered. Also, system selects tense randomly, let it be Vartamana (present). Now system extracts the endings for first person, masculine gender, plural number and present tense. To get this endings the system looks into the domain ‘rule-verb’. The search for the pratyaya in ‘rule verb’ results in a string ‘4: First: M: Anekvachana: Vartamana: to’. The last constituent is extracted from the above string which results in a pratyaya ‘to’. Now, this pratyaya ‘to’ is added in ‘phira’ that results in a verb ‘phirato (= phira+to)’. In this way the system generates legal and grammatically correct form of verb. Figure 2 shows the various sentence with two subjects and verb inflection as per the rules in table II.

Figure 1. Sample Sentence Template

TABLE II. Rule

1 2 3 4 5 6 7 8 9

VERB RULES FOR MULTIPLE SUBJECTS

Noun ‘X’ PX

GX

F * S/T * S * T * T # T M T F/N T F T N

Noun ‘Y’

NX

* * * * * * * * *

PY

GY

S/T * F * T * S * T # T F/N T M T N T F

Resultant Noun ‘R’

NY PR

* * * * * * * * *

GR

F F S S T T T T T

NR

* * * * # N N N N

P P P P P P P P P

The system uses similar technique as discussed earlier to get personified noun/pronoun. The search for second personified agent of action of third person, feminine in gender and singular in number results in a string ‘tee:Third:F:Ekvachana’. The first component ‘tee’ in this string is the second desired personified kartaa. Get the Conjunction and its details: The attribute of conjunction ‘type’ has value ‘C’. So system selects all the conjunctions of type ‘C’ from ‘domain-conjnction’. Let the list be { aaNi, va , an ... }. Now, system selects

III.

PEDAGOGICAL MODELS

Currently, our approach uses three different ways in teaching simple sentences namely generating variants of sentences, constructing right verb and constructing right kartaa. Generating variants is discussed in detail in earlier section only. We discuss now the construction of right verb. Construction of right kartaa follows similar approach and is not discussed here due to spaceconstraint.


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the correct verb form of ‘dhava’ here is ‘dhaavatesa (= dhaava + tesa)’. IV.

MORE RESULTS

It is mentioned in earlier section that the present DTD captures the simple sentence (with single and multiple subjects), and also compound and complex sentences to some extent. Here we present the results related to the compound and complex sentences.

Figure 2. Sample Output 1

A. Constructing Right Verb In construction of right verb, system generates the sentence without verb and asks the student to complete it with correct form of verb for specified dhaatoo. Along with this, it also provides the gender and tense. As mentioned in earlier section, to get the correct form of verb, one requires person, gender and number of the agent of action. As the agent of action is given in the sentence and gender is provided as a clue so one can get the correct person and number from it. Using these four components that is person, gender, number and tense, student can get the desired verb. We see now how system interacts with student during the construction of the right verb with the help of sample output as shown in figure 3. In sample output 2, the system asks student to complete the sentence with appropriate verb using dhatoo ‘dhaava’ with gender feminine and present tense. Here, the student has given the response ‘dhaavato’. The system generates its response by extracting the details for person and number of ‘tu’ along with the specified feminine gender. For this it looks into the domain ‘noun-details’. The search results in a string ‘tu:Second:F:Ekvachana’, which means that the person of ‘tu’ is second and number is singular. Now using all these four constituents i.e. second person, feminine gender and singular number along with present tense, system extracts the endings from the domain ‘rule-verb’. The search for an endings results in a string ‘25: Second:F:Ekvachana:Vartamaana:tesa’. The last constituents of this string is the endings which is added in the ‘dhaava’ that results in a verb ‘dhaavatesa (= dhaava + tesa)’. The system compares students response ‘dhaavato’ with its own ‘dhaavatesa’, which is wrong and accordingly it gives the feedback ‘verb form of dhaava is not correct’. Now system tries to identify what misconception about the agent of action ‘tu’ leads to the mistake. So system tries to find out how student got ‘dhaavato’. Looking at this verb, student has added ending ‘to’ in dhaatoo ‘dhaava’. The ending ‘to’ is available in present tense. But, it is for the agent of action of first person. It means that the student has the misconception about the person of the agent of action ‘tu’. Hence system asks the question ‘what do you think the person of tu?’. The student response is ‘Second’ which is correct and accordingly it informs to the student. Now, it checks his knowledge about the number of ‘tu’. The student response here is ‘Ekvachana’ (singular) which is also correct. From this system infers that the student knows about the person and number of the agent of action but he does not know the rule to get the right form of the verb. So system shows here the conversion rule: ‘If gender is feminine and tense is present whereas person is second and number is singular then the rule is add ending ‘tesa’. So

A. Compound Sentences A sentence which includes two main clauses and may also include one or more dependent clause is called as compound sentence. Here we considered two types of compound sentences where the first type has simple sentence followed by a conjunction and predicate. The second type can have simple followed by conjunction followed by simple sentence. The figure 4 shows these two types of sentences generated by the system. B. Complex Sentences A sentence which contains a main clause with one or more subordinate clauses which are joined with subordinating conjunctions is called as complex sentence. Here we considered complex sentences of two types. The first type has main clause followed by conjunction and subordinate clause. The second type has subordinate clause followed by conjunction and main clause. Main clause consists of subject and predicate whose attributes are similar to that of simple sentences. Subordinate clause has optional subject followed by predicate. The complex sentences are being dealt at a basic level. Here sentences like giving reasons have been covered. The figure 5 shows these two types of sentences generated by the system. V.

CONCLUSION

In this paper, we described the ongoing development of IELIL, a system for teaching Marathi language. As discussed earlier, till date the system supports student in learning simple sentences and also compound and complex sentences at the basic level. The sentence structure ensures that the system generates syntactically as well as semantically correct sentences only. The feedback system analyses the students’ mistake if any and provide adequate feedback messages which helps student to realize what mistake he has committed. The future plan of this ongoing research is to make sentence structure richer in the sense that it should take care of compound and complex sentences with the help of pedagogy model and the feedback mechanism. ACKNOWLEDGMENT The authors would like to thank Agarwal Pankhuri, Buche Tejaswini and Sherekar Sonal for extending their help in implementation and carrying out this research. REFERENCES [1]

M. Sasikumar and A. R. Joshi, “Intelligent instructional systems for language learning,” Proceedings of instructional conference on research trends in information Technology, Kolhapur, India, March, 2007.


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[2] [3] [4]

[5]

[6]

J. E. Ormrod, “Human Learning” 3rd ed., Upper Sadle River, NJ: Merill Prentice Hall, 1999. J. Gamper and J. Knapp, “A review of intelligent CALL systems,” Computer Assisted Language Learning 15/4, 2002, pp. 329-342. A. R. Joshi and M. Sasikumar, “A constructivist approach to teaching sentences in Indian languages,” International Conference on Advances in Computing, Communication and Control, Mumbai, India, Jan, 2009. W. J. Mathews, “Constructivism in the classroom: Epistemology; history, and empirical evidence,” Teacher Education Quarterly, 2003, 30(3), 51-64. B. A. Marlawe and M. L. Page, “Creating and sustaining the constructivist classroom,” 2nd ed., Thousand Oaks, CA: Corwin Press, 2005.

[7]

A. V. Faltin, “Natural language processing tools for computer assisted language learning,” Linguistik online, 17, 5/03. [8] S. A. Kazi, “Voca Test: An intelligent tutoring system for vocabulary learning using the mlearning approach”, 2005. [9] R. A. Close, “English as a foreign language,” London: Allen & Unwin, 1981. [10] Kinshuk, “Does intelligent tutoring have future!,” Proceeding of Inter. Conf. on Computer in Education, Ls Alamitos, CA: IEEE Computer Society, 2002. [11] J. Self, “The defining characteristics of intelligent tutoring systems research: ITSs care, precisely,” International Journal of Artificial Intelligence in Education, 1999.

Figure 3. Sample output 2




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