Purpose. The study began with a needs analysis that aimed at identifying the need to develop the mLearning implementation model for undergraduate English Language communication course based on students’ views. In order to achieve this aim, the needs analysis phase attempted to answer the following research questions:
1. What are the students’ perceptions on their language competence as preparatory for Professional and Communication Skills course?
2. What are the students’ perceptions on the traditional formal Professional and Communication Skills course?
3. What are the students’ mobile device capabilities and their level of mobile technology use?
4. What are the students’ level of acceptance and intention to use mLearning if incorporated into the formal Professional and Communication Skills course?
Answers to this question is crucial to justify whether there is a need to incorporate mLearning into their existing English communication skills course to assist the undergraduate language learning especially for the lower competent students to cope better with the course subject. In the incorporation of mLearning, this phase attempted to determine students’ acceptance of the intervention of mLearning as support to facilitate their language learning needs and their intention to use mLearning as extension to their existing formal language classroom learning. In short, the answers to these questions justified the development of the mLearning implementation model for the language course.
Sample of the study. This phase involved 220 undergraduate engineering students of a Malaysian private university who were undergoing an English Language communication course. Based on Cohen, Manion and Morrison (2007), samples numbering 30 and above are suitable for research study employing statistical analysis. The students were selected from the whole population of students who took the course subject ‘HAB 2033/HBB 2033- Professional and Communication Skills Course’ an undergraduate English for specific purposes course. Since the study attempted to develop the mLearning implementation model for the language course subject, purposive sampling method was used to select the students for the study. The course was offered as a compulsory elective subject by the institution to inculcate soft skills in
104 students to improve their competitiveness in the job market. The students need to complete the compulsory subject as fulfillment of a four-year undergraduate study. This course emphasizes the theory and practice of professional English Language communication at the interpersonal level, in teams and to a large group. The course serves to build upon the students’ academic and professional knowledge acquired through other core engineering or technical courses, and aiming at enabling them to be highly effective in expressing themselves and in imparting their professional and technological expertise in a variety of jobs, business, and professional settings. The whole course is designed for fourteen weeks offered in each semester and is divided into four parts: Process Description (Group Poster presentation), Technical Oral Presentation (Individual presentation), Business Meeting (Group presentation), and Persuasive Oral Presentation (Individual presentation).
Instrument of the study. The instrument used for this phase was a set of needs analysis survey questionnaire (refer to Appendix A). The questionnaire consisted of 48 questions divided into five parts: 1) Students demographic details and their perceived level of language proficiency, 2) Students’ perception on self-language competence, 3) Students’ perception on the current Professional and Communication Skills course, 4) Students’ use of mobile technology use, and 5) Students’ acceptance and intention to use mLearning. A pilot study was conducted on 70 undergraduate students from the same higher institution using the instrument to improve the questionnaire items. However, the 70 students were not included in the actual needs analysis study. Six (6) curriculum and instruction technology experts were referred to validate the instrument. Reliability test was conducted on the survey questionnaire for all items, which registers a Cronbach alpha coefficient of .872 as shown in Table 3.1.
Table 3.1
Reliability Testing of Needs Analysis Questionnaire
Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items
N of Items
.872 .829 64
The questionnaires were posed to the students to assess the students’ need to have a learning support in their formal language learning process as well as their level of acceptance on the incorporation of mLearning into their current formal language communication course and more importantly the degree of their intention to use mLearning. Although mLearning could be a viable support to cope with their language learning needs, the support could prove ineffective in the implementation later if the students resent the use of it (Sharples, Taylor, & Vavoula, 2005). The items for the survey questionnaire were constructed based on unified theory of acceptance and use of technology (UTAUT), a technology acceptance theory proposed by Venkatesh, Morris, Davis, and Davis (2003). UTAUT explains user intentions to use an information system (IS) and subsequent usage behavior. The theory posits that four key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) are direct determinants of usage intention and behavior (Venkatesh et al., 2003) as illustrated in Figure 3.1.
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Figure 3.1. Unified theory of acceptance and use of technology (UTAUT) by
Venkatesh et al., (2003). Adapted from “User acceptance of information technology: Toward a unified view”, by V. Venkatesh, M.G. Morris, G.B. Davis and F.D. Davis, 2003, MIS quarterly, p. 447.
Based on the key constructs, the items for the questionnaire were divided into eight expectancies:
1) Performance expectancy – In this study, performance expectancy dealt with the extent of the effectiveness of mLearning as a support in accommodating students’ language learning needs. For example, how students perceive the usefulness of mLearning in their learning process to accomplish learning tasks easily, and how mLearning could improve their learning productivity or even their course grades. 2) Effort expectancy – Effort expectancy is defined as the degree of ease in using
mLearning (Venkatesh et al., 2003).
3) Attitude toward using technology – This is defined as the student's overall affective reaction in using mLearning (Venkatesh et al., 2003).
4) Social influence – Social influence is defined as the degree to which an individual perceives how important others believe he or she should use mLearning (Venkatesh et al., 2003).
5) Facilitating conditions – Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of mLearning (Venkatesh et al., 2003).
6) Self-efficacy – Self-efficacy deals with the student’s individual perception on own ability and skills to use mLearning.
7) Anxiety – Anxiety deals with students’ apprehensiveness to use mLearning, for example, due to their concern on the uncertainties of what is expected of them in using mLearning.
8) Behavioral intention to use mobile learning – This deal with students’ eagerness and intention to use mLearning
Procedure. Needs analysis was conducted on the participants (undergraduate students) to assess their needs to develop the mLearning implementation model. Witkin (1997) defined needs analysis as a method to identify the gap between the current situation and targeted situation. McKillip (1987) on the other hand, stated that needs is a judgment value that a specific group has a problem, which needed to be solved. In the language field such as English for special purposes, needs analysis has long been identified as an important methodology used in educational planning (Benesch, 2001). Hyland (2005) argued that needs analysis could be classified as a technology in education, which can be employed at the preliminary stage of a language course, during the language course or post language course. Needs analysis, for instance could be used to gather data on a specified situation, which can be used as a basis to construct English for academic purposes course and language materials (Benesch, 2001). In the attempt to define needs analysis, Hutchinson and Waters (1987) identified three useful classifications of needs: necessities, lack, and wants. ‘Necessities’ refer to what needs to be learned to function effectively in a targeted situation. ‘Lacks’ refer to the gap
108 between what the learners already knew and the targeted proficiency while ‘wants’ is associated with subjective needs of the learners.
In the research on language needs, most studies are largely based on classroom settings mainly to improve classroom tasks (Marlyna, Siti Hamin & Mohamad Subakir, 2012). However, Zhu and Flaitz (2005) observed that experiences outside the classroom affect students’ overall academic performances where their interactions in a larger institutional context influence their in-class performance. Thus, it is necessary to investigate language skills needed for the students to perform beyond the classroom settings as findings from the study could dictate the types of suitable language activities in the classroom for effective language learning.
In the area of English for specific purposes, the literature has revealed at least two important aspects in the conduct of an effective language course or program: 1) the language course or program needs to accommodate not only the target needs but also the students’ learning needs (Momtazur Rahman et al, 2009; Vifansi, 2002). Target needs refers to the skills expected to be achieved as stated in the course outcomes and learning needs refers to students’ difficulties in attaining the goals of the course or program; and 2) the language course or program ought to consider both skills needed by students to fulfill academic tasks and perform job related activities after graduation (Bacha, 2003). In short, as students are end receivers of teaching and learning, their views and needs have to be considered in the design of a successful language course or program. Instructors, policy makers, or curriculum designers should not rely on their assumption that they have prior knowledge of students’ perception and needs on learning. For instance, through needs analysis, Bacha and Bahous (2008) in their studies on writing needs and language proficiency levels of students in business studies at the tertiary level revealed that students have higher satisfaction level on how they perceive their writing skills compared to their instructors’ perception. In another needs
analysis study on undergraduate petroleum engineering students, Al-Tamimi and Munir Shuib (2010) found that the students perceived that their current English course did not meet their needs and they could not use English effectively. They perceived that all language skills are important and they need continuous instruction and training to improve their proficiency. These studies indicated the importance of considering not only the institutional needs but also the students’ learning needs as well in the conduct of an effective course or program.
As described in the Chapter 1, the main issue of any English Language course for specific purposes is that the learning needs of the students at large were not effectively addressed in the conventional classroom learning to satisfy the course outcomes. The study seeks to investigate mLearning as a support to solve the problem. The needs analysis aimed at investigating existing issues and the need to develop the mLearning implementation model. The model could serve as a practical guide on how mLearning could aid in meeting the needs of the undergraduate language learners to acquire the communication skills through networking of language activities. The needs analysis in this study will be conducted via survey technique to identify the need for the mLearning implementation model based on students’ views. The participants of the study were given a set of survey questionnaires to respond to, in order to solicit their needs for mLearning.
Analysis of data. Data were analyzed using descriptive statistics via the Statistical Package for Social Science (SPSS) version 20 software. I propose the analysis of mode and mean scores for this phase to determine the needs of mLearning at the undergraduate level based on students’ views. Figure 3.2 shows a flowchart of the steps presented above to describe the methodology used for this phase. The main aim
110 of the results of the data was to justify the need to develop the mLearning implementation model.
Figure 3.2. Flowchart of Needs analysis phase.
Phase 2: Development of mLearning Implementation Model for Professional