DEVELOPING THE MOBILE APP

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CHAPTER 3: THEORETICAL FRAMEWORK

Introduction

The aim of this Chapter is to identify and introduce theoretical frameworks in line with the objective of the study with regard to vocabulary teaching and learning through mobile technologies. In other words, the Chapter is used to create “a theory base” (Hofstee, 2006: 92) for the objectives of this study. This Chapter maps the search for a suitable theoretical base for this study by surveying frameworks that could have worked, but were not used because of certain concerns. The Chapter then moves to detailing the Community of Inquiry as a framework for this study. The Conversation theories are also discussed in detail as supporting framework in this study.
According to Siemens (2005), theory provides a link between knowledge and implementation. In order to investigate the use of mobile learning technologies to enhance the vocabulary learning of students in an ODL context, a search was undertaken for a theoretical framework that would be suitable for the research objectives in this study while adhering to the pragmatist paradigm, especially with the emphasis on its instrumental view on knowledge as it is “used in action for making a purposeful difference in practice” (Goldkuhl, 2012: 8). As a developing researcher, it was also important for me to find a theoretical framework that would not only guide theory, but also the practice in the form of aiding research (data collection and analyses). In short, the search was for a theoretical framework that would “provide order and parsimony to the complexities of online learning” (Garrison & Arbaugh, 2007: 158).

Theoretical frameworks

Conversation Theory

In searching for a grounding framework, a myriad of theories was contemplated and Conversation Theory (CT) was considered as a possibility owing to the emphasis on technology‐mediated means of interaction, which are associated with technology‐mediated vocabulary learning in this study. The CT grew out of Gordon Pask’s work within Cybernetics. CT is “based on the premise that knowledge exists, is produced and evolves in action grounded conversations” (Boyd, 2004: 181). Through a series of conversations that may lead to new topics, the expert (who knows considerably more about the topic) and the learner are engaged in conversations that have to lead to an agreement about the area of discussion. Boyd (2004: 183) contends that this theory is “very difficult to grasp” and that it is not understandable without certain ideas from the fields of cybernetics, automata, formal linguistics, computer science concepts, theorems and notations, cognitive psychology and neurophysiology. Researchers in the field of education have taken the purported complicated theory and have applied it to examine the processes of learning with technology (Laurillard, 2002; Sharples, 2003).
While this is a groundbreaking theory, CT did not exactly fit in with this particular study. There was a concern with two aspects of the theory that were not compatible with the gist and context of this study. The first problem is that CT “is constrained so that all topics belong to a fixed agreed domain and the level of language of each action is specifically demarcated” (Boyd, 2004: 186). The subject matter as well as the context do not allow me to place such stringent restrictions on them. Discussions on language and its use cannot be thus constrained. Secondly, Pask’s CT involves an agreement at the end of the conversation, which is intimated to lead even to a deeper view of humanity (Boyd, 2004). While there are general agreements on vocabulary form, meaning and use, the eventual aim of the interaction is not that of reaching an ultimate agreement, but, rather, understanding. Sharples et al., (2005: 8) concur when they observe, “It does not mean that every concept must be negotiated and agreed”. For these reasons, CT did not entirely fit in with this study.

Connectivism

The other theoretical framework that was considered was Connectivism because it is touted as a learning theory for the digital age (Siemens, 2007) that is aimed at providing a better understanding and management of teaching and learning using digital technologies (Garcia, Brown & Elbeltagi, 2013). The idea of making connections within Connectivism was emphasised by Siemens who stated that “learning is a network phenomenon, influenced by socialization and technology … our need to derive meaning, gain and share knowledge, requires externalization” (2007: 10). Connectivism, thus, could have been suitable for this study because it emphasised shared experience in digital spaces. Miller and Doering support the idea of knowledge that transcends acquisition when they succinctly note that “it is neither sufficient nor possible to amass a store of content knowledge in order to be considered ‘learned” (2014: 10). In other words, knowledge has to be gathered collectively and shared or distributed. Distributed cognition, therefore, means, “No single individual is in receipt of all required knowledge to solve a problem or complete an activity alone” (Boitshwarelo, 2011).
There were three principles of Connectivism that were closely related to this study. The first one was that Learning and knowledge can rest in diversity of opinion. This principle was related to this particular study in that WhatsApp was planned to be used as part of the intervention as a platform where participants would share ideas and exchange opinions. The study also relied on mobile devices which, according to Boyinbode, Bagula and Ng’ambi, “allow for students to be connected and, thus learning content can be accessed and interaction can take place whenever learners need it, in different areas of life, regardless of space and time” (2011: 2). The second principle, that Learning is a process of connecting specialised nodes or information sources was also relevant because the mobile phones would allow for students to “access course content, as well as interact with instructors and student colleagues wherever they are located” (Gikas & Grant, 2013: 19). Finally, the principle that Learning can reside in non‐human appliances seemed pertinent since a single device offers many uses, which need to be tapped into for educational purposes (Rennie & Morrison, 2013). In this study, knowledge was found in cellphones on WhatsApp and VocUp.
Much as Connectivism had the potential for this study, there were three areas of concern. Firstly, there still exist concerns on whether or not Connectivism is a theoretical framework or a pedagogical view (Clarà & Barberà, 2014; Kop & Hill, 2008; Verhagen, 2006). While it could be argued that Connectivism is a relatively young framework and that perhaps it qualifies as such based on Sharples et al.’s (2005) guidelines for distinguishing a theory of learning which validate Connectivism as a theory of learning, there were still two methodological concerns that could not be disregarded. Connectivism could not offer clear guidelines for data collection as well as data analysis to help frame and articulate research. There was a need, thus, for a more guided framework for the study.
Based on these concerns, Connectivism was not used as a theoretical framework in this study. However, language learning in Connectivism is closely linked to Vygotsky’s theory of social interaction (1978). Vygotsky asserted that it is through social interaction, in this case using mobile technology, that experiences are turned into knowledge using language as a medium of negotiation. Through the guidance of a teacher or in collaboration with capable peers, students’ development is determined by interaction. Because of the link to interaction in digital spaces, this study leaned towards the task‐based approach to vocabulary development based on some guidelines of Task‐Based Language Teaching. Researchers such as Ellis (2003) and Nunan (2010) highlight how language is developed when learners use the target language in particular learning tasks. TBLT places emphasis on language learning through interaction in the target language, using appropriate activities (Nunan, 1991). The idea of interaction is further emphasised by Motlagh, Jafari and Yazdani (2014: 1) who assert that TBLT is « based on the use of communicative and interactive tasks”. Pellerin (2014: 5) stresses the importance of tasks by pointing out that task‐based approaches “promote the creative and spontaneous use of language through tasks and problem‐solving”. TBLT activities can be divided into Focus‐on‐Form (structural accuracy); Focus‐on‐Meaning (fluency); and Intermediate TBLT, which balances form and meaning. As Pellerin indicates, current language learning tasks that are technology‐mediated are highly organised and reflect pre‐determined outcomes.

Multi‐componential framework of word knowledge

A theory that was considered appropriate for framing the vocabulary teaching and learning section of this research was the theory of multi‐componential word knowledge by Nation (2001). Nation has argued, “Vocabulary growth is such an important part of language acquisition that it deserves to be planned for, deliberately controlled and monitored” (2002: 267). The planning, control and monitoring, thus, are all part of directly teaching vocabulary. The question, however, is how does one teach vocabulary. While teachers recognise the importance of vocabulary improving proficiency, many of them struggle with acquiring the skills of incorporating vocabulary teaching into their lessons (Read, 2000) so learners can understand new words. It is imperative to clarify, thus, what it means to know or understand a word. Thornbury (2002) states that knowing a word means knowing its form and meaning. Thornbury proceeds to list the components of knowing a word, including word class, meaning, word morphology, pronunciation, derivations, grammar, collocations, homonyms, polysemes, synonyms and antonyms, hyponyms, lexical fields, register, and style and variety, and connotation.
Knowing how to spell a word and knowing what it means is incomplete, however, if we consider that we need vocabulary to function in a language. Larsen‐Freeman (2003), while in agreement with the two components of knowing a word, adds a third element of knowing the word, the element of use. Knowing a word can, thus, be subdivided and allocated to the three categories, as listed in Table 3.1.
According to Larsen‐Freeman (2003), form refers to how the word looks and how it sounds. In the table above, word class and pronunciation point to the form of the word. Meaning refers to denotation, while use refers to meaning in particular contexts. Oxford and Scarcela (1994) support the inclusion of the third component of use, stating, “knowing an L2 word also involves being able to use the word communicatively in the context of purposeful interaction. » (1994: 232). When one says he or she knows a word, therefore, it means the person can recognise how it sounds or how it is spelt (for example, the difference between ‘vulnerable’ and ‘venerable’); one can derive the denotation of that word outside of a context (the difference between a boy and a girl) and that person can use that particular word in a way that suits context, audience and purpose. In short, Nation’s (2001) multi‐ componential word knowledge of form, meaning and use is supported by other researchers. This framework embodies the view of vocabulary in this research where words are taught and learnt with a focus on form, meaning and use. The multi‐componential word knowledge framework as portrayed in Figure 3.1 informed activities in this research in that the participants did not merely focus on spelling or knowing meanings of words, but they also worked on using the words appropriately.

Interaction Theories

The first theoretical framework that was found suitable for this study was the Theory of Transactional Distance which was defined as “the universe of teacher‐learner relationships that exist when learners and instructors are separated by space and/or by time » (Moore, 1993: 22). This theory defines distance not merely as spatial or temporal, but also as “pedagogic” (Moore 2007: 91). One of the main propositions of the theory is that as the number of dialogues increases, the distance between teacher and learner or learner and learner decreases. Because one of the main barriers to learning in distance education is the absence of interaction (Makoe, 2012), there is a consensus on the critical role of interaction for student support (Heydenrych & Prinsloo, 2010; Heydenrych, 2009; Tait, 2000; Moore, 1993). While “traditionally, interaction focused on classroom‐based dialogue between students and teachers” (Anderson, 2003: 129), interaction is currently a crucial aspect of non‐face‐to‐face teaching and learning contexts such as the ODL.
The driving principle in interaction is the mutual influence that people, and objects, exert on one another. It is important that stress is placed on the notion of mutual influence as it reveals interaction encapsulating the concepts of conversational dialogue (Holmberg, 1983); bi‐directionality (Moore, 1989), collective development (Heydenrych & Prinsloo (2010) as well as the need for social, cognitive and teaching presence (Garrison, 2007).
Interaction, therefore, exists as a reciprocal concept for the cognitive, affective and teaching support. While student‐student interaction presupposes that a student will be interacting with another student where Student A could be addressing Student B and Student B responding, other forms of interaction are at risk of being labelled as interaction when there is no real reciprocal, back and forth interaction involved. If mobile learning technologies are used to send bulk announcements to students without a mechanism for students to respond to the messages for clarity, should the need arise, the situation begs the question of how mutually interactive that scenario is.
There are different types of interaction, including student‐student interaction and student‐ lecturer interaction as well as student‐content interaction (Makoe, 2012). These interactions are all underpinned by the crucial role of technology in facilitating meaningful interaction (Garrison, 1989). For the purposes of this study, ‘technology’ refers to mlearning because of the benefits mentioned in previous sections linked to accessibility, cost‐effectiveness, broad ownership as well as flexibility. The notion of accessibility is stressed by Keegan (2005) who argues that availability to citizens is the main defining characteristic of successful technologies in distance education. If students need interaction for success in ODL, they need technologies which are accessible and portable. If over 90% of Unisa students are more likely to own a cellphone than any other technology (Makoe, 2012) then it is essential that one investigates how mobile learning can be used as an intervention for student support in general and for developing the vocabulary of students in particular.

Student‐student interaction

For many decades, the crucial role of peer influence through interaction has been acknowledged. In writing about student‐student interaction in schools, Johnson argued that “Experiences with peers are not a superficial luxury to be enjoyed during lunch and after school » (Johnson, 1981: 5). Johnson’s views on student‐student interaction leant more on socio‐emotional aspects, such as the interaction providing support for aspiration and motivation; contributing to social value and attitudes; influencing potential behaviours, and development of social roles. In contrast, Bernard, Abrami, Borokhovski, Wade, Tamim, Surkes and Bethel (2009) described student‐student interaction as « interaction among individual students or among students working in small groups » (2009: 1247). This definition shows a shift towards a purposeful type of interaction, focusing on working in groups.
Student‐student interaction has evolved, therefore, from a focus on socio‐affective support to include more cognitive influences such as explanation, argumentation and negotiation and mutual regulation (Sher, 2009: 1962). More recently, benefits of students working together include increased higher‐ order thinking, greater engagement, higher self‐esteem and higher test scores (Jacobs, Renandya & Power, 2016). Most recently, the explosion of social networking apps has seen educators exploring the use of social media to facilitate student‐student interaction.
Bouhnik and Deshen (2014) examined the use of WhatsApp for facilitating learning while Ferguson, DiGiacomo, Gholizadeh, Ferguson & Hickman (2017) integrated Twitter to facilitate online group journaling for postgraduate studies; Ventura and Martín‐Monje (2016) taught vocabulary through Facebook whereas DeSchryver, Mishra, Koehleer and Francis (2009) compared the use of Moodle against Facebook for group discussions towards enhancing social presence. These examples demonstrate that modern technologies, used for student‐student interaction, can develop reciprocity and cooperation among students through threaded discussions, bulletin boards and email applications (Beldarin, 2006). In ODL, with its characteristic physical and cognitive distance, facilitating student‐student interaction might seem an arduous task, yet the use of mobile technologies has been shown to facilitate peer interaction as shown further in this chapter. While ODL provides “exciting opportunities for not only increasing the reach of education and reducing its cost but, most important to us, for increasing the quality of teaching and learning” (Abrami, Bernard, Bures, Borokhovski & Tamim, 2011: 83), mobile learning activities have been prominently designed for learning settings different from the classroom (Frohberg, D., Göth, C. & Schwabe, 2009); thus mobile technologies are well suited for facilitating student‐student interaction in an ODL learning environment.

Human and non‐human interaction

Much as interaction is mostly coetaneous with human‐human engagement, with reciprocal effect, there exist two kinds of interaction relating to human and non‐human interaction where learners interact with content and devices. Student‐content interaction “is a defining characteristic of education” (Moore, 1989: 2) where the learner engages with educational content. While student‐student interaction is defined as an interaction between one student with another student, with or without the real presence of the instructor (Thurmond, 2003), student‐content interaction can also take place with or without the presence of the teacher or other learners. The ODL context is rife with student‐content interaction where learners « talk to themselves » about the information and ideas they encounter in a text, television program, lecture, or elsewhere (Moore, 1989: 2). This process is called didactic conversation (Holmberg, 1986) and there is a need for research into how students interact with content using mlearning technologies to facilitate learning in ODL.
Marquis and Rivas (2012) presented an analysis of a range of studies in mlearning from a variety of higher education institutions where they make use of mobile phones. One of those studies is by Lim, Fadzill and Mansor (2011) who described the Open University of Malaysia’s efforts of enhancing the blended learning approach for undergraduate distance learners with the successful implementation of mlearning through SMSes. The SMSes were used mainly for the university to communicate with the students, sending them announcements such as those pertaining to upcoming events and information on administrative changes.
Because these days student‐content interaction involves engagement with content through some form of technology, it stands to reason that student‐device interaction is explored.
Studies have demonstrated the use of mobile technologies for student‐content interaction where students engage with course content including assessment through technology (Başoğlu & Akdemir, 2010; Chen & Huang, 2007; Thornton & Houser, 2001). Student‐device interaction is thus referred to as student‐interface interaction where students have to interact with the gadget delivering the content before engaging with the said content (Hillman, Willis & Gunawardena, 1994; Makoe, 2012). While higher education institutions have employed the use of mobile devices for disseminating information on institutional and administrative information including announcements on deadlines, events, venue changes and other urgent messages (Keegan 2005; Traxler & Leach 2006), this study will explore how students interact with devices in order to interact with content towards learning through mobile phones.

Student‐teacher interaction

Another important aspect of interaction is that involving student and lecturer. According to Heydenrych (2009: 34), “the complete learning experience of distance education students is still dependent on sufficient interaction between student and educator”. Part of the core business of the university revolves around teaching students, and the lecturer still plays a pivotal role in that teaching. Makoe (2012) has illustrated the importance of the lecturer by presenting her different roles which include encouraging students, facilitating learning, correcting misconceptions as well as offering assistance. In ODL, where the lecturer cannot physically see her students every day and where the openness often means thousands of students, student‐lecturer interaction is limited (Makoe, 2012). If interaction is to take place, the lecturer needs considerable amounts of planning, flexibility and reinvention if she has to support her students effectively through interaction (O’Rourke, 2009).
A study that illustrates lecturer planning, flexibility and reinvention is presented by van Rooyen (2010). In this study, second‐year Accounting students at Unisa, an ODL institution were supported using Mxit, a social networking site. The lecturer for the Accounting module noticed that most students were registered on Mxit and were communicating with their peers. The lecturer decided to exploit the accessibility and wide availability of the platform allowing for interaction with students. The level of flexibility and commitment of the lecturer is evident in that students were invited to interact with him during the day, at night and on weekends. This openness tore down the distance of time and space. The interaction was bi‐ directional in conversations where the lecturer encouraged students and provided content‐ related assistance in real time.
Previous attempts at student‐lecturer interaction had involved planned group visits where lecturers would visit various regional centres to conduct face‐to‐face group sessions with students. The group visits were not successful as about 12% of students attended them. The low attendance was due to lack of interest as well as logistics related to the cost of travelling to regional centres or students not being able to get time off work (Prinsloo & van Rooyen, 2007). The introduction of Mxit, therefore, provided an accessible, faster and cost‐effective means for interaction that benefitted more students.
In this example, the affective support provided by the lecturer is evident in the encouragement and motivation provided to students, which help to alleviate stress (Rasheed, 2007). In addition, the interaction provided students with a sense of belonging, as they feel cared for (Makoe, 2012). The study above also demonstrates the cognitive support offered through student‐lecturer interaction in that the lecturer contributes to the understanding of course content. Where students struggle with misconceptions, the lecturer was readily available to clarify concepts. The systematic support leant again on the lecturer’s ability to plan and integrate course content in the interaction. An example of integrating course content is the ability of the lecturer to use Mxit in connection with the student portal, myUnisa.
The role of the lecturer in distance education is changing drastically (Cant, Wiid and Machado, 2013; Siemens, 2008; Bates, 2008; Brindley, 1995). The key consensus is that lecturers first need to acknowledge that there are ODL specific skills they need to develop in addition to being leading experts in their teaching fields. Such skills include fair and ethical behaviour and technical expertise (Cant, Wiid & Machado, 2013). From van Rooyen’s study, it should be added that the flexibility to engage boldly within the technological changes in the education and lifestyle landscapes is for the benefit of the student. The willingness and ability of the lecturer to commit to student‐lecturer interaction, however, also play a significant role in the affective, cognitive and systematic support of the students.

TABLE OF CONTENTS
DECLARATION
DEDICATION
ACKNOWLEDGEMENTS
ABSTRACT
KEY TERMS
ACRONYMS
CHAPTER 1: INTRODUCTION
1.1 Introduction
1.2 Background and Context
1.3 The problem
1.4 Research aim
1.5 Research questions
1.6 Methodology
1.7 Rationale
1.8 Research ethics
1.9 Definition of terms
1.10 Outline of thesis
CHAPTER 2: LITERATURE REVIEW
2.1. Introduction
2.2 The literature review
2.3 Conclusion
CHAPTER 3: THEORETICAL FRAMEWORK
3.1 Introduction
3.2 Theoretical frameworks
3.3 Conclusion
CHAPTER 4: DEVELOPING THE MOBILE APP
4.1 Introduction
4.2 Background
4.3 Steps to developing the app
4.4 App evaluation
4.5 Conclusion
CHAPTER 5: RESEARCH DESIGN AND METHODOLOGY
5.1 Introduction
5.2 Research design
5.3 Methodology
5.4 Methodology processes
5.5 Data storage
5.6 Reliability and validity
5.7 Conclusion
CHAPTER 6: PRESENTATION AND DISCUSSION OF FINDINGS
6.1 Introduction
6.2 Presentation of findings
6.3 Discussion
6.4 Conclusion
CHAPTER 7: SYNTHESIS OF FINDINGS, RECOMMENDATIONS AND CONCLUSIONS
7.1 Introduction
7.2 Synthesis and implications of findings
7.3 Proposed model for mlearning in Open Distance (and electronic) Learning
7.4 Implications of the study
7.5 Contributions of the study
7.6 Limitations of the study
7.7 Significance of the study
7.8 Recommendations for further research
7.9 Conclusion
7.10 Personal reflections
REFERENCES
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