Using the software Tell Me More to deliver a 90 minute-lesson
Using the application “Tell Me More English” to develop the speaking skill in a Saudi context where a group of learners have to take the PET test by the end of the year
The educational environment has been significantly altered by technology over the last three decades. Bax (2003) claims that there will be a time when technology becomes normalised in classroom instruction. He defines nomalisation as “the stage when a technology is invisible, hardly even recognised as technology, taken for granted in everyday life” (Op. cit., 23). Interestingly, by the advent of interactive smart boards and language learning software which can be run on tablets and mobile phones, computer presence in a language classroom has become conventional and familiar to both learners and teachers. At the turn of the last century, the number of teachers using Computer-assisted Language Learning (CALL) increased distinctly and many articles have been written about the role of technology in education in the 21st century (Lee, 2000). Owing to this change, nowadays, using computers to teach languages has become indispensable in many schools and universities since it is used to enhance teaching through using multi-media, the internet, online dictionaries and a variety of language learning programmes. According to Beatty (2013) CALL is “any process in which a learner uses a computer, and as a result, improves his or her language” (p,7). As such, a deluge of computer software has been created and developed as a tool for language self-learning. Although these computer programmes are designed for self-learning, they can be utilised and infused in language learning as a springboard to enhance learner’s linguistic skills. Warschauer and Healey (1998) argue that these software programmes enhance learners’ skills as they offer extensive practice to learners which cannot be achieved during the regular class time. Additionally, they expect teachers to provide guidance to learners on what skills and aspects they should work on (ibid).
Tell Me More (TMM) is an advanced self-learning and asynchronous learning tool that offers a solution for language learning. According to Gyamfi and Sukseemuang (2017), an apparent advantage of TMM is its capacity to cover the learning content of various activities to practise reading, speaking, listening, reading and writing. Furthermore, TMM provides learners with exercises which foster interaction through speaking and ordinary activities of vocabulary and grammar, designed to suit authentic events (ibid). What makes it interesting and user-friendly is the speech recognition which allows the user to listen and converse with the computer via a microphone (Perez, 2014). To continue the conversation, the user has to select one of the three or four answers provided by the system and utter it clearly within a few seconds; the answer turns green if it is recognised by the software (Figure 1&2). The computer replies immediately with either American or British accent, depending on the choice of the user when they install the software. Moreover, the software contains some oral drills of the sentences, phrases and vocabulary used in the conversations to improve the intonation and pronunciation which are represented in graphical feedback (Figure 3). The user can also choose the level from beginner, intermediate, upper-intermediate to advanced. It is also claimed that the new version’s exercises are aligned with the Common European Frame Reference (CEFR) (Penner and Grodek, 2014).
(Figure 1) screenshot from TMM. A real video with a man talking to the user and waits for the answer. The video resumes after the user responds
(figure 2) screenshot from TMM
The user responds by reading out one of the answers. If it is pronounced correctly, the software recognizes it and turns green
(Figure 3) sentences of the conversations can be practised and the user can see their improvement through the graph and the chart on the right with their voice recorded
In Algonquin College, Jizan in Saudi Arabia where I worked from 2013 to 2016, the learners are admitted to the college in order to specialize in a technical diploma. Before they enter the diploma, they have to study English for one academic year as part of what is called the Preparatory Year Program (PYP). By the end of the PYP program, the learners have to attempt and pass the PET Test with a minimum of 50%. The majority of the learners who enter the college are described as ‘absolute beginners’ and they can hardly hold a conversation or speak about themselves in English. According to Cambridge Assessment, speaking allocates 25% of the test (English, tests and Preliminary, 2018); learners are assessed on fluency, pronunciation and using a variety of vocabulary and grammar. Although the learners are familiar with most of the test questions, the speaking test causes anxiety for many of them due to the lack of confidence and being questioned and recorded by an interlocutor, who is usually a native speaker of English. Since the college has a language laboratory equipped with a headset and can be booked for practicing online PET tests, using Tell Me More (TMM) with these group of learners can adventurously help them improve their communicative competence.
Firstly, using Tell Me More can immensely help learners build confidence when they speak. Speaking is a productive skill which require learners to produce not only sentences but also sounds which could be different from their first language. Horwitz et. al, (1986) claim that learning a foreign language in a classroom can be stressful. As such, many learners avoid participating in speaking class activities due to some factors attributed to anxiety (ibid). They highlighted the difference between three types of anxiety: communication apprehension, (oral) test anxiety and fear of negative evaluation (p,127). Gardner and MacIntyre (1993) maintain if language anxiety continues to increase, it becomes a trait and affects the performance of the learner. This is described by Oxford (1999) as “debilitating anxiety” which can demotivate learners and impact negatively on learner’s attitudes towards the language (p,60). Whilst CALL helps reduce learner’s anxiety (Han and Keskin, 2016), the speaking activities which are designed to interact with the computer can help learners, who feel shy or have anxiety of losing face due to their mistakes, gain confidence. TMM allows learners to listen and respond to semi-authentic speech by a native speaker which can lower their affective filter and start building their own confidence.
Secondly, Tell Me More can develop speaking fluency of the learners. Fluency is considered as part of communicative competence; it is defined as “the ability to link units of speech together with facility and without strain or inappropriate slowness or undue hesitation” (Hedge,1993:275). It is also one of the criteria the learners are evaluated in the oral exam of PET. It is argued that Communicative Language Teaching (CLT) has been developed to promote fluency which could be sometimes over accuracy (Hunter, 2011). Similarly, the ‘Interaction Hypothesis’ stresses on the negotiation of meaning by focusing on form and giving corrective feedback in a form of recast or modifying the speech (Lee, 2008). Additionally, Computer Mediated Communication (CMC) allows learners to engage in interaction, receive comprehensible input and corrective feedback, however, it is claimed that it encourages fluency rather than accuracy (Hunter, 2011). This is one of the most pressing issues when it comes to communication of whether to favour fluency over accuracy or vice versa. In fact, TMM could resolve this issue since it is a computer program and it cannot accept errors, fluency and accuracy will be equally enhanced. The speech recognition requires the user to be accurate as well as fluent by articulating the provided sentences fully in a few seconds to continue the conversation.
Thirdly, as far as speaking concerned, pronunciation is considered to be of paramount importance to any communicative activity, which can be improved by the courseware of TMM. Pronunciation stresses on the articulation of sounds, vocabulary and intonation; mispronunciation in any language could lead to misunderstanding. The software offers a wide range of oral activities which allows learners to listen to a native speaker articulating words, phrases and sentences (Neri et. al, 2008). The learners can record themselves and the sound recognition compares it to the pattern and gives automatic feedback presented in a graph. Furthermore, they can practise as many times as they need and see how their pronunciation is improving by playing back the previous recording (Gyamfi and Sukseemuang, 2017). In fact, this is compatible with Schmidt’s ‘Noticing Hypothesis’ which emphasizes on how learners acquire the language by noticing certain features in the input and noticing the gap, of how the output differs from the input (Ellis, 2008). Although Krashen rejects any kind of language output because it is ‘anxiety-provoking’ (ibid), it could be argued that the software cannot cause anxiety to learners since they are interacting with a machine. The listening and repetition activities with feedback also assimilate the ‘pushed output’, proposed by Swain (Swain, 2005).
On the other hand, despite the advantages which TMM could offer to language learners, TMM has some limitations, that associates with using CALL software in general.
Although TMM seems to enhance learners’ speaking skill by exposing them to structured conversations with native speakers, artificial intelligence cannot act as human intelligence. Human speech is usually affected by emotions, facial expressions, body movements and eye contact, namely the’ Implicit Channel’ which disappears with human-computer interaction (Cowie et. al, 2001). The speech recognition is one of the main issues which has been reported by some users. Beatty (2013) claims that computers are still inaccurate when it comes to synthesize human voice despite the rapid development of CALL. He also reports that computers cannot deal with complex speech which might produce disappointing results to the users. In fact, this issue appears in the advanced level of TMM; the users have to respond with complex sentences which have to be produced in a few seconds and as such the software would not recognize it. Thus the natural language processing cannot be fully aided by machine intelligence.
The software does not allow learners to use a real authentic speech. The users of this program are only restricted to some answers provided by the system and they only need to read out one of them in a few seconds in a correct manner. This means that the users are not actually interacting in genuine conversation but only reciting some sentences. In order to learn a language, the learner has to engage in communication and also make mistakes Rubin (1975). In this program the users do not need to produce any language of their own; therefore, they could be considered as ‘passive participant’ (Little and Venkatesh, 1994). In real conversations, the interlocutor responds to the speaker by backchannelling or discourse markers to express their emotions. Moreover, in the real KET or PET oral test, the interlocutor rewords the question if the testee does not understand the original one, namely ‘back-up questions’ (Taylor and Galaczi, 2011). This one of the issues of CALL in general and TMM in particular as there is no room to negotiate the meaning except the one provided by the software.
Another issue with TMM is that it might promote rote language learning, not understanding the exact meaning. Although some users might manage to interact with the software, they might have problems with the meaning of the sentences they utter because the software requires users to only articulate the sentences. Furthermore, the exercises are the same which means the user can expect and memorize the conversation every time they log in. This could be a reverse to the behaviourist method -audio-lingual- where the learners have to follow certain patterns of dialogues and errors have to be corrected (Liu and Shi, 2007). This audio-lingual method has been criticized as the skills which the learners gain might not be transferable outside the classroom (ibid). As a result, the users of TMM might be accustomed to this habit of memorizing certain pattern of conversations which might not support them in some real situations.
In conclusion, TMM can be useful to language learners who lack confidence of speaking and have problems with pronunciation. It also can create an engaging virtual learning environment outside classroom with the interactive exercises for learners who use English as a foreign language. Although the exercises seem to be repetitive, in considering the golden rule of ‘practice makes perfect’, TMM can still be beneficial to enhance the communicative competence to the beginner level of the aforementioned context. This lends support to the findings of Hashim and Yunus (2012) and Gyamfi and Sukseemuang (2017) who concluded that the users of TMM of beginners and advanced level improved their pronunciation, speaking, listening and reading skills. However, teachers should have enough training and be aware of how to integrate technology into their lessons and when to use TMM. One of the drawbacks of technology is that it can have a negative impact on both learners and teachers if it is used unsuccessfully in a lesson. Therefore, teachers may avoid technology and throw the baby out with the bath water even though there are plenty of advantages that technology could offer to enhance language learning. Perhaps Gündüz (2005) was right when he reasoned that computers are “the servant of the users, and thus should not be forgotten their roles in teaching is solely a teaching aid” (P, 197). Teachers then have to think of how to utilize computers and all available technologies to achieve the desired goals of their learners as well as the institution, bearing in mind that they are only a tool which should be driven by the teacher in the right direction.
Bax, S., 2003. CALL—past, present and future. System, 31(1), pp.13-28.
Beatty, K., 2013. Teaching & researching: Computer-assisted language learning. Routledge.
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W. and Taylor, J.G., 2001. Emotion recognition in human-computer interaction. IEEE Signal processing magazine, 18(1), pp.32-80.
English, C., tests, E. and Preliminary, B. (2018). B1 Preliminary exam format | Cambridge English. [online] Cambridgeenglish.org. Available at: http://www.cambridgeenglish.org/exams-and-tests/preliminary/exam-format/ [Accessed 16 Apr. 2018].
Ellis, R. (2008). The study of second language acquisition (2nd ed., Oxford applied linguistics). Oxford: Oxford University Press
Gardner, R.C. and MacIntyre, P.D., 1993. A student’s contributions to second-language learning. Part II: Affective variables. Language teaching, 26(1), pp.1-11.
Gündüz, N. (2005). Computer Assisted Language Learning, Journal of Language and Linguistic Studies, 1,(2), 193- 294
Gyamfi, G. and Sukseemuang, P., 2017. Effect of Tell Me More on EFL undergraduate students’ English Language achievement. Open Praxis, 9(4), pp.449-461.
Han, T. and Keskin, F., 2016. Using a mobile application (WhatsApp) to reduce EFL speaking anxiety. Gist: Education and Learning Research Journal, (12), pp.29-50.
Hashim, H. and Yunus, M.M., 2012. Tell Me More: Issues and Challenges. Advances in Language and Literary Studies, 3(2), pp.136-146.
Hedge, T., 1993. Key concepts in ELT. ELT journal, 47(3), pp.275-277.
Horwitz, E.K., Horwitz, M.B. and Cope, J., 1986. Foreign language classroom anxiety. The Modern language journal, 70(2), pp.125-132.
Hunter, J., 2011. ‘Small Talk’: developing fluency, accuracy, and complexity in speaking. ELT journal, 66(1), pp.30-41.
Lee, K.W., 2000. English teachers’ barriers to the use of computer-assisted language learning. the internet TESL Journal, 6(12), pp.1-8.
Lee, L., 2008. Focus-on-form through collaborative scaffolding in expert-to-novice online interaction.
Little, T.D. and Venkatesh, D., 1994. Prospects for interactive video-on-demand. IEEE multimedia, 1(3), pp.14-24.
Liu, Q.X. and Shi, J.F., 2007. An Analysis of Language Teaching Approaches and Methods–Effectiveness and Weakness. Online Submission, 4(1), pp.69-71.
Neri, A., Mich, O., Gerosa, M. and Giuliani, D., 2008. The effectiveness of computer assisted pronunciation training for foreign language learning by children. Computer Assisted Language Learning, 21(5), pp.393-408.
Oxford, R.L., 1999. Anxiety and the language learner: New insights. Affect in language learning, pp.58-67.
Penner, N. and Grodek, E., 2014. Integrating Multimedia ICT Software in Language Curriculum: Students’ Perception, Use, and Effectivenes. The EuroCALL Review, 22(1), pp.20-39.
Perez, V.A., 2014. Effectiveness of Tell Me More in enhancing communication skills. Asia Pacific Journal of Multidisciplinary Research, 2(6), pp.164-172.
Rubin, J., 1975. What the” good language learner” can teach us. TESOL quarterly, pp.41-51.
Swain, M., 2005. The output hypothesis: Theory and research. In Handbook of research in second language teaching and learning (pp. 495-508). Routledge.
Taylor, L. and Galaczi, E., 2011. Scoring validity. Examining speaking: Research and practice in assessing second language speaking, 30, pp.171-233.
Warschauer, M. and Healey, D., 1998. Computers and language learning: An overview. Language teaching, 31(2), pp.57-71.