Customization of CALL-SLT - CALL-SLT in the SLA Classroom

CALL-SLT in the SLA Classroom

5.4 Customization of CALL-SLT

In the previous chapter we discussed the architecture of CALL-SLT (Chapter 4), and in the first part of the current chapter we discussed ways in which this tool can be integrated into traditional classroom teaching. We also highlighted the importance of having customized tools that take into account all aspects discussed earlier in this chap-ter – most importantly the target group, the students’ language level and an appropriate pedagogical approach. This is why we customized the already existing CALL-SLT tool (as used for courses discussed in Rayner et al. (2010b); Bouillon et al. (2011b); Bouillon et al. (2011c)) to assure the course’s relevance for classroom teaching. The best way to guarantee relevance is to include a subject matter expert in the development process, such as a language teacher or a pedagogical expert. In the following sections, we will ad-dress how we proceeded and what resulted from our collaborations with such an expert.

To create the customized CALL-SLT course discussed hereafter, we worked in close collaboration with an English teacher and school director at a lower secondary school in Muttenz, Baselland, in the German-speaking part of Switzerland. The teacher was involved in the development and customization process, helping us with subject matter expertise and pedagogical advice, as well as advising us on his expectations of a CALL tool that is relevant to the Swiss language curriculum.

This collaboration resulted in the development of a more elaborate course design compared to earlier courses, using a dialogue structure plus multimedia and gamifica-tion elements, as will be discussed in the following subsecgamifica-tions.

5.4.1 English Course “Ready for English”

As mentioned above, we worked in close collaboration with a subject matter expert to customize CALL-SLT for our target group. The discussions with the English teacher quickly revealed that he was much more interested in training the students’ commu-nicative skills (c.f. Chapter 2, subsection 2.3.2) rather than having pure vocabulary or grammar drill exercises. This communicative approach to English learning makes sense in the given context, since students are usually advanced enough after their first year of English to train such conversational competences. This is probably due to German and English being closely related, and to the fact that the students’ interest and motivation to learn English is in general high, since not only many movies and popular music are in English, but English is also the most commonly used business language in Switzerland (next to the respective local languages).

In order to guarantee the course’s relevance, we opted to base the content of our lessons on an English textbook that is commonly used in Swiss schools. More concretely we based our content on the manual used at our partner school, which is the first volume of the “Ready for English” textbook (Morrissey et al. (2001)).

As we have already seen in Chapter 1, Swiss cantons currently still use different teaching material and textbooks (and they will as long as the HarmoS agreement is not fully implemented). Since we nevertheless wanted to find a compromise between customizing the CALL-SLT course to the textbook and making it portable to other cantons, we – together with our subject matter expert – decided to create lessons that are loosely based on the book, using the scenarios and vocabulary taught in the text-book but not sticking too closely to details, such as person names, etc. As the tables in Appendix B show, two commonly used English textbooks (“Ready for English” and

“New Snapshot”) both cover many of the same topics in their first year volumes. This makes it possible to develop a course that can be used in combination with various text-books and that can hence be used in various cantons. Once the HarmoS agreement is implemented it would, however, certainly be worth reworking the lessons to link them more closely to the final textbook. In order to assure our strategy, we additionally spoke to a teacher in the canton Basel-Stadt (where the “New Snapshot” textbook is used) who confirmed that our lesson content would be useful for his students, even if

the textbook’s content varies slightly.

5.4.2 Lesson Development

After a first meeting in which the framework of the collaboration was discussed with the participating language teacher, the lessons were created in several steps. The school wished to have lessons that imitate a trip to London, with individual scenarios covering various topics treated in the first year’s textbook lessons. The idea was to create thematic units that imitate real-world conversations and to build dialogue scenarios for each of those units. With this approach we wanted to encourage learners to actively hold a conversation with a (virtual) native speaker and hence to train their receptive, productive and communicative skills at the same time. An overview of the eight lessons that we developed for our customized CALL-SLT course is given in Table 5.2.

In addition to the separate lessons being used individually, they could optionally also all be linked together in a logical order, creating one large lesson that simulates an entire trip to London. The idea here was that the pupils could first train their skills in smaller and well-controlled sequences (c.f. Skinner’s Small Step Principle discussed in Chapter 3, subsection 3.2.1) and, once they feel more at ease, they could then attempt the final challenge with one long scenario.

The idea behind using such dialogue scenarios was influenced by a number of SLA theories, as discussed in Chapter 2. One of the earliest theories that mentioned the pos-itive influence of training interactional skills was the Interaction Hypothesis developed under the cognitivist approach to SLA (discussed in Chapter 2, subsection 2.3.1.1).

The functional school (c.f. Chapter 2, subsection 2.3.2) continued to put a major fo-cus on communicative skills, stating that pragmatic communicative needs foster L2 acquisition. The sociocultural approach (c.f. Chapter 2, subsection 2.3.3) additionally stated that L2 acquisition is supported if language is used in meaningful and social context. This theory also stressed the importance of the L2 acquisition process being an interactional and social act that is best encouraged by face-to-face interaction.

By using meaningful and real-life dialogue scenarios we tried to accommodate both the pragmatic needs as well as the aspect of using language in context. The use of video

Table 5.2: CALL-SLT lesson overview

Lesson Scenario Topics

Train station buying a train ticket to Lon-don

name, nationality, numbers, locations, time expressions Meeting someone small-talk on the train with a

foreign person

name, nationality, siblings, capitals

Tube station buying a tube ticket numbers, locations, prices Hotel asking for a hotel room numbers, room types,

facil-ities, prices, payment types, where-questions, complaints Tourist office buying tickets for different

at-tractions, such as musicals, museums, etc.

numbers, cultural knowledge of London, time expressions, ordinal numbers

Restaurant ordering food and drinks at a restaurant

prompts (as discussed in Chapter 4, subsection 4.5.2.3) also helped to imitate face-to-face interaction in a virtual environment, taking the social component into account by imitating real-life scenarios.

5.4.2.1 Dialogue Structure

For the dialogue design we first prepared a corpus with all possible interactions for the different scenarios, which was then revised by the English teacher. He added some more content and gave valuable feedback on how dialogues should best be structured in order to build an application that is well suited to train English communicative skills at a beginner level. As a second step, we grouped the sentences in the corpus to produce a first draft of the actual dialogue structures for each scenario, making both linear and branched dialogues possible. An example of a dialogue flow draft for the

“Hotel” lesson can be seen in Figure 5.1. Once these dialogue flows were elaborated, they were again discussed in detail with the participating teacher, and some more turns were added where necessary. Only once the teacher was perfectly satisfied with the content and turns of the dialogue scenarios did we implement them into CALL-SLT.

In order to implement the new lessons developed on paper, different steps must be taken. Besides adding the necessary vocabulary and grammar rules (c.f. Chapter 4, subsection 4.2.2), we also wrote the dialogue scripts, which was done in an XML notation. We created a separate XML script for every lesson, containing a number of dialogue steps that can be combined in various ways to build different paths through a given dialogue (with varying levels of difficulty). Every lesson typically contains between 10 and 20 steps, with a total of 622 prompts across all eight dialogues. Table 5.3 shows the repartition of the total number of available prompts per lesson.

Each dialogue step is documented with the following information: (1) unique ID, (2) recorded multimedia file, (3) group of prompts, (4) next step depending on different conditions. The example given below shows the second step in the hotel lesson, in which the video prompt asks how many nights the student would like to stay at the hotel:

Figure 5.1: Dialogue draft for the “Hotel” lesson (light grey = video prompt; white = text prompt; dark grey = user’s response)

Table 5.3: Total number of different prompts per lesson

<step>

The unique ID (1: ask_for_number_nights) identifies the step so that it can easily be referred to in connected steps; the multimedia field (2: how_many_nights) refers to a video file, which contains the cartoon clip asking the student a question in the L2 or giving an answer to the preceding interaction (here: “How many nights would you like to stay?”); the group of prompts (3: room_for_number_of_nights) identifies all possible text prompts that can be used in the current dialogue step, as defined in the corpus. In the example given above, it could be any corpus entry that corresponds to the interlingua representation Frag: Zimmer f¨ur X N¨achte (Ask: room for X nights).

In practice it could be any of the following corpus entries (where every syntactical structure can be used in combination with any duration from one to seven nights):

sent(‘i would like a room for one week’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i should like a room for one night’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i would like to stay for two nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘a room for three nights please’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘can i have a room for four nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘could i have a room for five nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘could you give me a room for six nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘could you please give me a room for seven nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘do you have a room for two nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘have you got a room for three nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘give me a room for four nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i am looking for a room for five nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i need a room for six nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i want a room for seven nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i will have a room for two nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

sent(‘i will take a room for two nights’, [default, english_course, group(hotel)=room_for_number_of_nights]).

The <repeat> instance defines which step the student goes to if his or her answer was rejected by the system. This is usually the same step, meaning that the student is asked to repeat his or her answer (4: ask_for_number_nights). If a student’s an-swer is not accepted upon his or her third attempt, he or she is redirected to an easy

yes/no question (<limit>) in order to limit frustration (4: is_one_night_okay). In our example the receptionist asks if a room for one night is okay, to which the stu-dent responds with “yes” to be redirected back to the standard dialogue flow. The two <success> tags refer to steps that the student can be lead to if the answer was successfully accepted by the system. In the dialogue step shown in our example, the student moves on to the stepnot_availablewith a chance of 25%, where the cartoon character tells the user that no room is available and simultaneously asks if a room for one night would be okay as well. In 75% of all cases, however, the student proceeds to the next logical step in the dialogue, which isask_type_of_roomwhere the student needs to indicate the type of room he or she would like.

If we want to further increase the difficulty level, we can add steps to the dialogue in which the students need to disagree or negotiate with their virtual conversation partner. In an example from the “Restaurant” lesson, the waiter brings the user the wrong dish and he or she has to complain, or maybe even ask to talk to the manager.

This scenario is displayed in the following five-step example:

1.

<step>

<next_repeat>sorry_correct_food</next_repeat>

4. <!-- Manager: I can offer a 10% discount or we will

cook the right food, but that takes 30 minutes. -->

<step>

5. <!-- Manager: Perfect, I hope you enjoy the rest of your stay.

Can I help you with anything else? -->

<step>

In step one we have a conditional <next_success> step, which is chosen in 50%

of all cases (probability="50") and only in a more advanced level (cond="level

>=silver"). In this case the waiter insists that she has brought the correct food (step two) and the student has to start negotiating by asking for the manager (step three).

In step four the student then speaks to the manager, discussing whether the correct food should be brought or if a 10% discount is acceptable. In step five the student then returns to the normal dialogue, continuing by ordering dessert.

There is also the option of adding meta-information to the corpus entries in order to condition the choice of text prompts. In an example from the “Shopping” lesson, the user might ask for boots in the beginning of the dialogue (Frag: Stiefel). For the next prompt, in which the salesperson asks for the user’s color preferences, we can add a matching text prompt sayingFrag: braune Stiefel(Ask for: brown boots). The mechanism allowing for this consistency is defined directly in the corpus, restricting the choice of the second prompt to anything having the same meta-information for first_article, which in this case is boots.

sent(‘i am looking for boots’, [default, english_course, group(shopping)=article, first_article=boots]).

sent(‘i would like brown boots’, [default, english_course, group(shopping)=colour, first_article=boots]).

An example of a complete scripted dialogue for the “Shopping” lesson as well as the corresponding corpus entries can be found in Appendix C.

5.4.2.2 Prompts

As discussed with the English teacher of the participating school, the prompts for this course were designed in a combination of a video and text prompt (c.f. Figure 4.5 in Chapter 4), in order to imitate a real-world conversation as closely as possible. Because the video prompts were recorded by native English speakers, pupils could train not only their productive skills, but also their receptive skills in the L2.

One wish that was brought forward by the English teacher was for the course to be adaptable in its difficulty. The solution we developed for this request was twofold. On

the one hand, we introduced branched dialogue flows as well as additional negotiation steps (c.f. subsection 5.4.2.1); and, on the other hand, we tried to add diatopic varia-tions to the video prompts. While some lessons use a cartoon character with a voice recorded by a native British speaker, who speaks in a clear, easily understandable En-glish, other lessons are recorded by Irish or Scottish natives, which naturally increases the difficulty of the receptive part of the exercise.

5.4.2.3 System Feedback

As far as system feedback is concerned, we opted for plain color feedback in combi-nation with an adequate reaction from the cartoon conversation partner instead of indicating the source of the error, which might be irritating if the recognition result is not completely reliable. If the student’s response is successfully accepted by the sys-tem, a green frame is visible around the text prompt box and the dialogue advances to the next logical step. If the student’s response is incorrect, a red box is displayed and the cartoon character responds by asking the student to repeat his or her answer (e.g.

“I’m sorry but I didn’t understand you. Could you please repeat that?”). If the student makes a third attempt and his or her answer is still not accepted by the system, the dialogue moves on to a yes/no question (e.g. “I’m sorry but I didn’t understand you.

Is X okay?”).

5.4.2.4 Gamification Elements

For our customized English course we also decided to introduce some basic gamification elements, as have been described in Chapter 4, section 4.7. The idea was to make the course more appealing to our target group and to try to increase their motivation to use the application based on the motivation theory discussed in Chapter 2, section 2.4.

An extensive evaluation of this hypothesis is discussed in Chapter 7.

Every lesson started with a score credit of 100 points and students could gain points for various structures, which are listed below. Conversely, points were deducted if the help function was consulted (note that the help function was only available for the two

lower levels “plain” and “bronze”), or if the students’ answers were rejected by the

According to the scheme displayed above, students received 1 point for asking

“Where is the science museum?” and 3 points for using the more complex question structure“Can you tell me where the science museum is?”

We used a set of four badges, namely “plain”, “bronze”, “silver”, and “gold”, which were linked to the fulfillment of criteria concerning lesson completion and – for higher levels – a minimum score value, as indicated in Table 5.4.

Table 5.4: Badge criteria for English dialogue game

plain bronze silver gold

score n.a. n.a. 90 100

lesson completion 1 3 3 3

The design of the badges was customized to the various lessons, showing an icon that was greyed out until the student acquired a badge. For badges linked to lesson completion, a badge was colored proportionally for every lesson completed until the badge was fully colored and hence completely acquired. Figure 5.2 illustrates an exam-ple from the restaurant lesson for which the plain badge has been acquired, the bronze badge has been partly acquired and the silver and gold badges are still greyed out.

Figure 5.2: Badges for the “Restaurant” lesson

5.5 Summary

In this chapter we introduced various aspects that are important for the successful customization and introduction of CALL-SLT in the SLA classroom.

We started with the investigation of our target group and their language level: in our study we were targeting lower secondary school students with an age range of 12 to 16 years and an L2 level between A1 and B1. More precisely, we were aiming our study at students in German-speaking Switzerland studying English at a beginner level.

In section 5.2 we discussed the L2 skills that we could expect for every level (A1, A2 and B1). We looked in particular at communicative competences, such as linguistic, sociolinguistic and pragmatic competences, where our main focus lay on vocabulary control and range, grammatical accuracy, phonological control and fluency. We

Dans le document The Potential of Interactive Speech-Enabled CALL in the Swiss Education System: A Large-Scale Experiment on the Basis of English CALL-SLT (Page 156-174)