For many years, language teachers have consistently used technological aids in teaching foreign languages in different learning institutions. Technological aid has often come in the context of computer-aided teaching through the support of the World Wide Web to create a comprehensive online learning experience for students (Levy, 1997, p. 1). Online English learning encompasses a number of commonly known information communication technology applications that facilitate the process of learning. These applications have been synonymously used in other contexts such as virtual organizations, distance learning, social networking sites, concordances and the likes (Hewer, 2010). In more easily comprehendible contexts, ICT tools have been used in recent times to facilitate the working of mobile phones and other hand held devices such as iphones and ipads.
From the progress of technology, teachers have in the past used technological devices like gramophones, slide projectors, film grip projectors, video cassette recorders, DVDs and the likes (to facilitate tutor-student communication), however, in the current century, many teachers are known to be using multimedia labs for teaching English as a second language to foreign students(Davies, 1982, p. 3). However, with the advent of the World Wide Web (www) in the 90s, online language learning has taken a new dimension, and currently, applications such as web browsing, mosaics and the likes have been applied in education to transform online language learning. In addition, the worldwide web is known to have eased the exchange of information between students and learners because a mere click on an image or icon gets a user the needed information.
For many years, many tutors have been using computer-assisted language instructions in online English learning (before the advent of the computers assisted language learning) and this is because most teachers at the time held the belief that students learn better through succinctly following teacher instructions (Cameron, 1989, p. 69). However, this view changed with time, and this is after most tutors realized that a teacher-centered approach was not going to supply the desired outcome in online learning. In its place, they chose to pursue a student-centered approach (Chris, 2010, p. 1). This concept has evolved over time and acquired new names, but it is important to note that all versions of computer-assisted learning techniques have all been characterized by a strong sense of student empowerment (Cameron, 1989, p. 69).
This means that computer assisted learning techniques stress a lot on the importance of students learning on their own (even though it also incorporates a two-faced directional strategy which also focuses on individual input) (Young, 2007, p. 1). Comprehensively, many teachers have found various types of online application techniques quite useful for providing students with comprehensive learning experiences because to a large extent, these applications emphasize what the students have already learnt; or for weak students, it can act as a supplementary tool for future understanding. This study provides a detailed analysis of the application of online learning in English as a second language, through an analysis of the most commonly used applications
Understanding English language in an unfamiliar surrounding is often a daunting task but Chatbot application enables students to horn their skills in English grammar and conversational skills (Nicks, 2010, p. 1). The Chatbot system is an artificial intelligence application where students are provided with English language practice in a typed, chat or spoken word manner through the use of a technology called the Natural Language Processing (NLP) (Remenyi, 2010, p. 217). The application has also been used by a number of organizations to help their staff better comprehend the dynamics of English language.
Comprehensively, the Chatbot system is a technique designed or programmed to give specific answers to a predetermined set of questions. The predetermined set of questions does not however work to its disadvantage because from the overall use of the technique, the system can be able to improve through learning from previous exchanges (Nicks, 2010, p. 1). In this manner, the appropriateness of its response is greatly improved. Nonetheless, the technique uses natural language processing to process inputs and outputs (Pan, 2008, p. 181). The system also varies from simple to more sophisticated types of softwares which are practically difficult to distinguish from a scenario where the tutors and students learn in a non-virtual environment.
Businesses and organizations normally use the Chatbot system to reduce the overall overheads they incur in sustaining their operations (Nicks, 2010, p. 2). This is true because most organizations have come to acknowledge the fact that finding cheap English tutors is not easy and paying for one-on-one time, between an employee and a tutor is not effective in an organizational setting because there are usually too many learners (staff) requiring tutor support (especially in organizations where the staff may need additional ESL skills). Moreover, it is well know that organizations usually suffer losses in man-hours because they often have to use organizational time when training their staff. This is where the Chatbot application comes in handy because it allows learners to openly interact with their tutors at any time of the day and in a brief manner, such that, a lot of time is saved in ESL teaching (Nicks, 2010, p. 2).
An Online TESL (Teacher of English as a Second Language)
An online TESL is one of the many applications found in a Chatbot system. One of its greatest advantages is that it greatly facilitates student learning of conversational skills (Nicks, 2010, p. 3). The system application is basically offered at no charge but it can only work via chart (Nicks, 2010, p. 3). Through the frequent exchange of questions and answers between the student and the system, the student can easily improve his or her grammar skills (and the overall comprehension of English) in a wider context. The program can however work in two ways: through text and voice (Nicks, 2010, p. 3). The text format can easily improve the student’s English writing skills while the voice format can easily improve the student’s English conversational skills. From previous understanding of how the Chatbot system works, we can conclude that the online TESL only works through a natural language processing system.
The Chatbot application system normally uses natural language processing systems to program its operations (Nicks, 2010, p. 4). Through this process, the system can easily convert human language styles into data that can be easily comprehended by the system; for instance, a user may easily scan handwritten information in a sheet of paper and feed it into the system, after which the computer will read the image and store the relevant data accordingly.
The natural language process therefore enables the system to decipher all relevant data in the system and match it with related data in its database to come up with relevant pieces of information which can be used by the students (Nicks, 2010, p. 4). The system also does the same for sounds because it is able to match certain human sounds with characters (from its database) to come up with an appropriate response that the user will find useful.
Learning Management Systems and Web 2.0
Learning Management Systems has over the years grown to be an important tool in English comprehension. Learning Management Systems doesn’t necessarily have to reproduce the effects of traditional modes of learning because it works by empowering students to develop some sense of independence in English speaking (through a virtual environment) (Arena, 2010, p. 1). Learning management systems are meant to boost student productivity and facilitate free exchange of information (implying learnt materials amongst them). Elements such as ownership, audience and availability of resources are therefore normally understood from a “student-collective” context as opposed to the traditional method of understanding (denoting an institutional point of view) (Arena, 2010, p. 1). This is done with the help of an e-tutor.
Nonetheless, learning management systems are normally effective when their application is coupled with web tools. An online learning tool is basically defined by the term “tool”, because it should serve a specific pedagogical purpose in enhancing student understanding of the English language through an exchange of information and through facilitating tutor student connections (Arena, 2010, p. 2). English online tools are therefore tailor-made to facilitate optimum interaction of the English students in a good learning environment. For instance a learning tool can be able to integrate the same type of information from a blog, voice thread and another site (say, YouTube) so that learners draw more engagement from the topic of analysis.
The learning management system is especially useful in online English learning application when used with Web 2.0 tools because the adopted tools majorly seek to improve the students’ oral comprehension of English (Arena, 2010, p. 2). However, certain aspects to the system were also built with the intention of preserving some of the most core areas of English learning (when students interact with the system) (Solomon, 2007). Moodle is an integral component of the learning management system because it seeks to offer guidelines to the students when undertaking their learning programs, however, more importantly, it acts as the focal point of student-system interaction (Arena, 2010, p. 2). This application seeks to eliminate the commonly abstract learning process associated with most online English application techniques which give little room for tutors to interact with their students because it uses Web tools such as blogs, wikis, voice threads, and graphic organizers (and the likes) to provide a comprehensive learning experience for students (Arena, 2010, p. 3).
In this system, students can openly interact with each other and view each other’s oral videos but at the end of it all, the students are nonetheless able to be the custodians of their individual works. A great emphasis is made on improving the student’s oral comprehension skills because the systems explores the potential lying in the student’s diverse learning styles through the tactful balancing of predetermined tasks (of the entire process and how the tasks will be accomplished) (Arena, 2010, p. 3). Moreover, the system also taps into diverse student learning styles by appropriately scheduling student-learning practices. The tools web 2.0 provides also come in handy when the editing and publishing phases are easily carried out (on the audio and text files) to improve the competency students have in digital or technological proficiencies, articulated in e learning (a basic skill needed in any e-learning process) (Arena, 2010, p. 4). This segment cuts a niche above most ESL application processes because students are empowered with the basic technological skill needed in the 21st century skill requirement profile; thereby improving their chances of succeeding in professional and educational spheres (concurrently).
Many students and professionals who have used Learning Management Systems and Web 2.0 systems for long periods have identified that the optimal application for this system when analyzed in the context of a curriculum:
“….is not driven by predefined inputs from experts; it is constructed and negotiated in real time by the contributions of those engaged in the learning process. This community acts as the curriculum, spontaneously shaping, constructing, and reconstructing itself and the subject of its learning in the same way that the rhizome responds to changing environmental conditions” (Arena, 2010, p. 4).
When analyzed from a practical point of view, the students are bound to seek a well-defined structural organization to effectively carry out their e-learning activities. In addition, from practical experiences of the application, it has been agreed by many professionals in the past, that:
“In the design of the Listening Plus course, despite the affordances of social construction of knowledge and connections within the group, students consistently request a more guided type of learning to make them feel safer while exploring their online course on listening, which is for many of those students, their first formal online learning experience” (Arena, 2010, p. 5).
This means that even if students were objective enough to pursue their own goals in English online learning, their lack of experience in the institutional learning environment is bound to pull them back. The learning management system therefore provides the guidance needed for these students to wade through uncertain learning waters.
Human Language Technologies
In the past, human language technologies have been used to facilitate the exchange of information between tutors and students; especially in an environment where several languages are being spoken (Dodigovic, 2005, p. 124). Human language technologies (as a technique) were specifically started by the European Commission to enable students learn English as foreign language, especially in areas dealing with speech synthesis, speech recognition and parsing (Gupta, 2010, p. 1). Since its application in various platforms across Europe, speech synthesis among students has significantly increased, especially because the system encompasses an online dictionary which requires learners to know how to pronounce various words (Gupta, 2010, p. 1). The system has even been further improved by the fact that human language technologies gives an output of human voice which is almost identical to the authentic human version of it (Gupta, 2010, p. 1).
However, human language technologies normally has a problem of enabling students to properly construct sentences and phrases because it experiences challenges solving problems of intonation; consequently leading to the outcome of unnatural sentences (Davies, 2002). Human language technologies is however beneficial to people with poor sight because it encompasses text-to-speech embodiment which enables blind (or partially blind) learners to learn English efficiently (Gupta, 2010, p. 1). The system also encompasses automatic speech recognition processes, but this has been identified to suffer a couple of challenges as can be evidenced by Ehsani (1998, p. 45) who identifies that:
“Complex cognitive processes account for the human ability to associate acoustic signals with meanings and intentions. For a computer, on the other hand, speech is essentially a series of digital values. However, despite these differences, the core problem of speech recognition is the same for both humans and machines: namely, of finding the best match between a given speech sound and its corresponding word string. Automatic speech recognition technology attempts to simulate and optimize this process computationally”.
The automatic speech recognition provides a predetermined model which articulates specific sounds that students are supposed to imitate; however, there are some inconsistencies which may lead to sound rejection even though the student may try to match the sound to the best of his or her ability (Waibe, 1990, p. 200).
In addition, the human technology process is known to have assisted students in sentence construction through the “parsing” component which articulates a given tree structure of sentence composition, defining (to students) the different components of a sentence (Matthews, 1994, p. 533). To some extent, this technique is also used by most tutors to have a clear understanding of input and diagnostic errors which students often make in online English Learning. However, previous versions of parsing could not solve this problem because they were unable to pinpoint areas requiring correction and neither could they identify important branches to assist in remedial activities (Heift, 2007).
In this regard, parse’s proficiency in analyzing errors has been a common issue of debate because its proponents advocate that natural language processing and human language technologies support the technology to comprehensively analyze errors, however, critics often point out the fact that the technology cannot be relied upon when analyzing its proficiency (from the point of view of language teaching) (Matthews, 1994, p. 533). To remedy the rift between the different categories of tutors, it has been often identified that artificial intelligence techniques may close the gap between different theoretical views of parse (Last, 1989, p. 153). Nonetheless, in the same conflict, other researchers still say that artificial intelligence techniques threaten basic principles of humanity (Underwood, 1989, p. 71).
Málflækjan is a specially designed game that is played by multiple players in a social environment meant to improve players’ English proficiency. The game is usually played by four players using 73 cards (which have an attribute and a related value attached to them) (Sigurþórsson, 2010, p. 6). After shuffling the cards, each player is given a card and one of them is randomly chosen to be the tangler (Sigurþórsson, 2010, p. 6). The tangler gets the privilege of authorizing the next player to go on with the game after picking an English sentence of approximately four to nine words (Sigurþórsson, 2010, p. 6). Sigurþórsson (2010, p. 6) further explains that:
“Each time a player has a turn, (s) he can choose to apply one or more of his/her cards to a single word on the table or draw two more cards. If the player chooses to apply cards to a word, (s) he puts them beside the appropriate word and announces his action. Then the tag of the word is checked against the attribute on the cards that the player applied. For every card that matched, the player gets points equal to the points on the card, but for every card that did not match the player gets a single penalty point regardless of the point value of the card”.
The game usually goes on until all the cards are exhausted from the deck, after which a winner is determined. The biggest advantage to this application is that it is unique to all other online learning applications because it is fun and more engaging (Sigurþórsson, 2010, p. 6). The four players can be sourced from a number of locations through a networked system of interaction (Sigurþórsson, 2010, p. 6). This attribute pits the online application game as one of a kind because never in the history of natural learning processes and English learning has such an application been designed (Sigurþórsson, 2010, p. 6). However, the main aim of designing the game is to help students horn their skills in linguistic proficiency (in a fun and challenging environment) because the social aspect to the game greatly increases the level of engagement (but at the same time, manifesting challenges experienced in a practical social environment). The users can also monitor their progress in learning skills because they can tabulate their performances over time and come up with an overall analysis of the progress made. The users can also sit back and device ways through which they can improve their progress.
Ice tagger has over the years been used as a morphological analyzer because it looks up each word in a lexicon and provides results detailing its findings from its database (Percy, 1996, p. 237). However, if there is a missing link between the information requested and its memory’s data, ice tagger then tries to estimate the correct outcome by approximating the correct tag file through morphological analysis. Later, Sigurþórsson (2010, p. 8) explains that:
“Local rules are used to eliminate tags that are illegitimate based on local context then heuristics are used to change tags that are not in nearest neighborhood. IceTagger uses the same tag set as in IFD and obtains 91.5% tagging accuracy, measuring the whole tag string, on IFD”.
Even though this technique achieves a high rate of tagging accuracy, it still remains lower than the average level of tagging required for most online English application softwares (Sigurþórsson, 2010, p. 8). However, some scholars note that the level of accuracy depicted in this study can be further improved if the data output outlay is further subjected through the system (Salakoski, 2006, p. 643). One of the biggest advantages of this technique (in comparison to all the others) is the high level of detail the technique provides.
The English online application techniques identified in this study seek to comprehensively improve the linguistic proficiency levels of students. However, when we analyze their application, we note that various techniques have their own unique sense of identities and advantages which can only be beneficial when tutors correctly research the contexts they are to be used before application.
For instance, the Málflækjan technique is quite important for students willing to enforce their already acquired linguistic skills by perfecting them through the game. This type of technique is therefore not useful for new learners. When we analyze the applicability of the ice tagger technique, we come up with the conclusion that the technique is prone to a lot of noun errors (Sigurþórsson, 2010, p. 8). In the same context, human language technologies pose a problem when trying to enable students properly construct sentences and phrases because it has challenges solving problems of intonation; consequently leading to the outcome of unnatural sentences. On the contrary, Human language technologies are also beneficial to people with poor sight because it encompasses text-to-speech embodiment which enables blind or partially blind learners to learn efficiently.
The moodle technique which is a part of the learning management system also seeks to eliminate the commonly abstract learning process associated with most online English application softwares, which give little room for the tutors to interact with their students (because it uses Web tools such as blogs, wikis, voice threads, graphic organizers and the likes to provide a comprehensive learning experience for the students). Lastly, this study points out that the Chatbot system is useful to learners because it helps them comprehend what is to be done in an unfamiliar surrounding. Nonetheless, we note that from the analysis of all these techniques, there is an absence of a clear comprehension of how these techniques relate or compare to each other. This is the new frontier for future research studies because it is important for students and tutors to know which techniques apply at which stage of the learning process and what impacts they have.
Arena, C. (2010). Listening Plus: A Social Approach to Learning English Online. Brazil: Casa Thomas Jefferson Binational Center.
Cameron. K. (1989). Computer Assisted Language Learning: Program Structure and Principles. London: Intellect Books.
Davies, G. (1982). Computers, Language and Language Learning. London: CILT.
Davies, G. (2002). Article on CALL in the Good Practice Guide at the Website Of The Subject Centre for Languages, Linguistics and Area Studies (LLAS). Southampton: University of Southampton.
Dodigovic, M. (2005). Artificial Intelligence in Second Language Learning: Raising Error Awareness. New York: Multilingual Matters.
Ehsani, F. (1998). Speech technology in computer-aided language learning: strengths and limitations of a new CALL paradigm. Language Learning and Technology, 2(1), 45–60.
Gupta, P. (2010). Information and Communications Technology for Language Teachers (ICT4LT). Slough: Thames Valley University.
Heift, T. (2007). Errors and Intelligence in CALL: Parsers and Pedagogues. New York: Routledge.
Hewer S. (2010). Introduction to Computer Assisted Language Learning (CALL). Slough: Thames Valley University.
Last, R. W. (1989). Artificial Intelligence Techniques in Language Learning. Chichester: Ellis Horwood.
Levy, M. (1997). CALL: Context and Conceptualization. Oxford: Oxford University Press.
Matthews, C. (1994). Intelligent Computer Assisted Language Learning as cognitive science: the choice of syntactic frameworks for language tutoring. Journal of Artificial Intelligence in Education, 5(4), 533-56.
Nicks, V. (2010). Use AI Chatbots for English Language Practice. Web.
Pan, Z. (2008). Technologies for E-Learning and Digital Entertainment: Third International Conference, Edutainment 2008, Nanjing, China, 2008: Proceedings. New York: Springer.
Percy, C. (1996). Synchronous Corpus Linguistics: Papers from the Sixteenth International Conference on English Language Research on Computerized Corpora (ICAME 16). New York: Rodopi.
Remenyi, D. (2010). Proceedings of the 5th European Conference on e-Learning (ECEL 2006). Dublin: Academic Conferences Limited.
Salakoski, T. (2006). Fifth International Conference on Natural Language Processing. New York: Springer.
Sigurþórsson, H. (2010). Using Icenlp in Computer-Assisted Language Learning Applications. Web.
Solomon, G. (2007). Web 2.0: New Tools, New Schools. New York: ISTE (Interntl Soc Tech Educ).
Underwood, J. (1989). On the edge: Intelligent CALL in the 1990s. Computers and the Humanities, 23, 71-84.
Waibe, A. (1990). Readings in Speech Recognition. New York: Morgan Kaufmann.
Young, L. E. (2007). Teaching Nursing: Developing a Student-Centered Learning Environment Lippincott. London: Williams & Wilkins.