extfieldsofstudy: Psychology Medicine

s2fieldsofstudy: Education Medicine

text:
Facilitators and Barriers of Assistive Technology and Learning Environment for Children with Special Needs

The purpose of this research was to study the facilitators and barriers of assistive technology (AT) and the learning environment for children with special needs in special education schools in Chiang Mai, Thailand. The informants were one hundred and sixteen children with special needs, who studied in nursery to Grade 12, or with their caregivers. The instrument was a questionnaire applied by the International Classification of Functioning, Disability and Health (ICF) and examined for content validity by five specialists. The results in terms of AT showed that a majority of children with physical disability needed it for mobility and use of school buildings and those with hearing disability for communication. However, most of the children did not need to use AT for culture, recreation, or sports, while many considered it as a facilitator for education. In terms of the learning environment, most characteristics of the physical environment were facilitators for children with special needs, as were those of the social environment for all groups of such children. The results of this study were useful in providing information for AT and design of a learning environment relating to the varied characteristics of children with special needs in special education schools.

Introduction
Education is an occupation performance area that is meaningful for children, in that the school system can provide direct impact on their occupation [1]. Children with special needs require a specific education program and related services that encourage them to learn with peers in schools and participate in social activities as children do in general. Children with special needs include those with visual, hearing, physical, and intellectual disability, autism, and language and communication disorders as well as multiple disabilities. They have various needs depending on severity and context. Some children with disabilities are encouraged to study in mainstreaming schools, where they meet their needs successfully, but others do not. A number of children need more specific help and services in order to be suitable for study in special education schools.
Assistive Technology (AT) is any product, instrument, strategy, service, or practice used by people with disabilities and also the elderly. It is produced specially or availed generally to prevent, compensate, relieve, or neutralize impairment, disability, or handicap and improve the individual's autonomy and quality of life (QOL) [2]. AT users vary from children to elderly people. The purpose of AT use depends on age relating to requirement, lifestyle, and the environment in which it is used [2]. Children with special needs can use AT as a way of improving functions and encouraging learning, especially when participating in a school environment. In the school year 2015-2016, survey data of the US Department of Education indicated that a higher percentage of students aged between 3 and 21 years received special education services [3]. This high percentage showed that most of these students had learning disabilities and studied in general schools. The trend for mainstreaming and inclusive education schools is increasing in order to encourage and enable students with special needs to have the same learning environment as that of their peers without disabilities [4]. However, due to policy and socio-cultural factors, most children with special needs in Thailand enroll in special education schools, including those for the blind and with hearing, physical, and intellectual disability. Students in special education schools are provided AT from the Ministry of Education. AT items are found in schools that are related to the disabilities of their students, for example, magnifiers, Braillers, slates, and styluses in schools for the blind; hearing aids, cochlear implants, and sign language interpreters in schools for students with hearing disability; walkers, crutches, canes, wheelchairs, and Augmentative and Alternative Communication (AAC) in schools for students with physical disability; and Computer-Assisted Instruction (CAI) and educational board games in schools for students with intellectual disability [5]. Some studies reported that all types of AT items were provided to students with disabilities in special education schools. Students with visual disability were given and used more AT items than other students with disabilities, in both special education schools and universities [5,6]. Moreover, students with visual disabilities needed AT the most, while those with hearing and physical disabilities did not need it so much [6]. Previous studies surveyed AT provision, problems, and needs, but they did not study or explore the facilitators and barriers of AT for students in the context of their learning environment. Although matching equipment with the needs and characteristics of disabled students is not always difficult, understanding the nature of their tasks or functions and environment and preferences is necessary [2]. Thus, the perspectives of students with special needs on AT as facilitators and barriers are useful for service providers when choosing, planning, and encouraging these people to reach their potential.
A learning environment refers to the diverse physical locations, contexts, and cultures in which students learn [7]. Thus, the learning environment is not only the typical classroom but also a collaboration area that can be characterized as a place for active interactions between learner and instructor or between learner and other learners [8]. Some studies found that the learning environment, especially the classroom, had a positive impact on the academic performances of the students [9,10], and it is very important for those with special needs. A positive learning environment can facilitate the performance areas in daily life, education, play, leisure, and social participation for the students. On the other hand, a negative learning environment can obstruct their performances and skills.
International Classification of Functioning, Disability and Health (ICF) was adopted in 2001 by the World Health Organization (WHO) [11]. The ICF provides a common language to describe human functioning in people with and without disabilities. In fact, it can be a model that guides selection measures, treatment goals, and outcomes [12]. The definition of "environmental factors" in the ICF is a component that makes up the physical, social, and attitudinal environment in which people live and conduct their lives [11]. These factors include products and technology, natural environment and human-made changes to the environment, support and relationships, attitudes and services, and systems and policies. These environmental factors may act as facilitators and also barriers in meaningful activities, in which persons are able to participate [12].
The context in this study was placed on special education schools for students with disabilities, including schools for the blind, deaf and hard of hearing, and those for students having physical and intellectual disability. These schools are placed in urban areas, except for those for physical disability, which are located in suburban areas. The students are selected for admission to a school by type of disability. In addition, the schools have an evaluating team that generally comprises special education teachers, occupational therapists, and school administrators who consider minimum performances that students are expected to have, particularly regarding communication and self-care performance. Most of the students are provided AT by the rights of children with disabilities, as indicated in the Acts of the Ministry of Health. However, in some cases, students still do not receive AT; therefore, the Individual Education Plan (IEP) team plays a role in considering suitable AT for students through the rights in the Acts of the Ministry of Education. School buildings for students in need are designed in the same way as general schools and each one has three to four floors. Not all of them have elevators. However, the buildings of the school for students with physical disability do have slopes between each floor and wide walkways to facilitate wheelchairs and walking aids for the students.
In fact, AT and the learning environment are able to improve the study and participation of students with disabilities to the level of their peers. However, different types of students have varied needs. As AT and the learning environment are considered as both facilitators and barriers in schools where disabled students can participate in learning and social activities, this study is aimed at exploring these factors for providing appropriate AT and setting a suitable learning environment for these people.

Methods
This research studied the facilitators and barriers of AT and the learning environment for students with disabilities in special education schools, in Chiang Mai, Thailand. The participants were selected by stratified random sampling from all four special education schools in Chiang Mai such as those for students with physical, intellectual, hearing, and visual disability. They comprised 116 students with disabilities, including 10 kindergarten, primary, and secondary students from each school, except for only 6 kindergarten students from the school for the blind. Most of the students were boarders, but some attended day school. Students who had multiple disabilities were not included in this study.
The instrument used was a questionnaire that applied to the ICF and consisted of two parts: AT and the learning environment ( Table 1). The questionnaire also was examined for content validity by five related specialists. The AT part (IOC = 0.91) included 63 items and comprised five types of AT such as that for mobility (IOC = 0.93), communication (IOC = 0.93), education (IOC = 0.94), culture, recreation, and sports (IOC = 1.00), and school buildings (IOC = 0.88). The learning environment part (IOC = 0.95) consisted of two types: physical (IOC = 0.94) and social environment (IOC = 1.00).
There were four qualifier scales in the questionnaire (F = Facilitator, B = Barrier, NO = No Opportunity, and NA = Not Applicable). An AT facilitator was defined as a convenience with positive effect on accessibility to learning and socializing activities in the school context. An AT barrier was defined as an inconvenience with negative effect on accessibility to learning and socializing activities in the school context. No Opportunity was defined as students who were Table 1: Applying ICF to the questionnaire on facilitators and barriers of assistive technology and the learning environment.

ICF domains in environmental factors
The questionnaire aware that AT was useful for them, or they needed to use it, but did not have the opportunity to do so. Not Applicable was defined as students who did not consider AT as being necessary. The definitions of these four qualifier scales were explained to the participants individually. An individual interview was carried out directly in the data collection process by using the questionnaire with students who had good communication skills in the Thai language. However, if the students were too young or had communication problems, the caregivers or teachers gave the interview instead. All of the kindergarten students and the adults responded to the questions in this study. Most of the primary school students (70.00%) and all of the secondary ones responded to the questions by themselves. Twenty-four adults responded to the questions on behalf of the kindergarten and 30% of the primary school students, including 8 special education teachers, 4 occupational therapists, and 12 guardians. The special education teachers and occupational therapists responded on behalf of more than one student. The conditions under which data were collected consisted of an individual interview in a quiet room. The time-span for completing the questionnaire was about 30-45 minutes. If the students or informants did not know about any type of AT, the researchers and two research assistants could give more explanation and present pictures. Then, data were analyzed by descriptive statistics for examining and summarizing the facilitators and barriers of AT and the learning environment for students with disabilities in special education schools.
The results in terms of AT showed that most of the students did not need to use it for communication, mobility, or use of school buildings, except for those with hearing and physical disability. Most of them did not need to use it for culture, recreation, or sports, and most considered AT for education as a facilitator. In terms of AT for mobility (as shown in Table 3), most of the students with physical disability perceived it as a facilitator, especially the standard wheelchair. On the other hand, many of them had no opportunity to use highly functional mobility aids such as an electric wheelchair, a scooter, or hand bicycle. In addition, many students with visual disability perceived a white cane and reflective tape as facilitators. These AT items were necessary mobility devices for students with visual disability, but it was interesting that some of them had no opportunity to use them, which were nonetheless low-tech devices that were not difficult to provide.
In terms of AT for communication (as shown in Table 4), most of the students with disabilities realized the importance of this AT as a facilitator, particularly those with hearing disability, as they probably needed it to help them. It was interesting that many students with visual disability considered AT for communication as a facilitator, even though they had no obvious communication barriers. When considering detail, this study found that most students with visual disability perceived AT for communication as a facilitator for written communication.
In terms of AT for education (as shown in Table 5), most of the students perceived this AT as not necessary for this purpose. However, most of those with intellectual disability indicated that concentration training software was a facilitator for them in education, while students with physical disability needed AT items for participating in classroom educational activities such as pen and notebook, concave table, and touch screen computer.
In terms of AT for recreation (as shown in Table 6), many of the students perceived the importance of AT for recreation as a facilitator. At the same time, many of them had no opportunity to use it, except for those with visual disability. This finding showed that although this AT was in the educational context, the students perceived it as necessary.
In terms of AT for school buildings (as shown in Table 7), most of the students with physical disability reported that this AT was a facilitator, particularly the guide post for all types of these students. However, even though a low number of students with visual disability reported on AT for school buildings, they indicated that the permanent and movable slope was a barrier for them. This information is useful, as it shows that not all AT is a facilitator for all students with disabilities. It depends on what their disabilities are. That is to say, some AT is able to be a facilitator for some types of students with disabilities but not for others. There are two parts in terms of the learning environment, including the physical and social environment. Results for the physical environment showed that it was mostly a facilitator for students with physical disability, except for the colored bands on school building stairs, braille keypads, and elevators, which were not applicable. The physical environment for students with intellectual disability was mostly a facilitator, except for obstacles in school buildings, such as the barriers of slopes and steep stairs. In addition, the students had no opportunity to use the colored bands on stairs or elevators in school buildings. The physical environment for students with hearing disability was mostly a facilitator, except for the barriers of slopes and colored bands on school building stairs. In addition, results indicated that stairs, slopes, colored bands on stairs, classroom doors, steps in front of classroom doors and position of handles in a classroom, use of the doorknob in the classroom, weight of a chair, and use of an elevator in a school building were not applicable. The physical environment for students with visual disability was mostly a facilitator, except for the elevator, which the students had no opportunity to use (as shown in Table 8).
In terms of the social environment, all four groups of students with special needs perceived it as a facilitator (as shown in Table 9). However, except for those with physical disability, the students perceived this AT as a barrier in relation to the school context. Furthermore, some students with intellectual and hearing disability reported that help from assistant teachers was not necessary for them.

Discussion and Conclusion
This study is aimed at exploring the facilitators and barriers of AT and the learning environment for students with special  needs in special education schools in Chiang Mai, Thailand, by using a questionnaire that applied to the ICF. The results of this study found not all students indicating AT for mobility as a barrier. Most of these AT items were perceived as facilitators by the students with physical disability, while no students with hearing disability saw them as such. On the other hand, most AT items for communication were perceived as a facilitator for students with hearing disability. These findings indicated that not all AT is necessary for all students with disabilities, but necessity depends on their type of disability [12]. Students with physical disability have the problem of general mobility, while those with visual disability frequently need AT to help them move and gain access to where they need to communicate and participate in social activities [13,14]. In fact, students with communication difficulty, such as those with hearing and intellectual disability as well as cerebral palsy, also need AT for accessing and participating in their daily activities [15]. However, some AT items might be a facilitator and barrier simultaneously. For instance, while AT for school buildings might be a facilitator to enable access for students with physical disability, it might be a barrier in the case of those with visual disability. Therefore, awareness of the students' perspectives should be considered, and they should be able to participate in the process of AT design and provision if possible [16]. These findings relate to previous studies [5,6], which also indicated that most AT items were perceived as facilitators by students with disabilities, but their necessity depends on the students' disability. For example, the slopes in school buildings are a facilitator for students with physical disability, while being a  barrier for students with visual disability, although slopes are designed universally for all people. When considering the design of slopes in school buildings for blind students, this study found that most slopes had no rail on either side. For this reason, some blind students might fall down when walking along the edge of a slope. In other words, whether AT items are facilitators or not depends on how they are able to compensate for disability, and this is why awareness of the students' perspectives should be considered in the process of AT design and provision. Besides AT being provided for all students with disabilities, there must be awareness of quality and design. Moreover, the findings showed that students reported NO for most AT items or they did not have the opportunity to use high-tech AT. Students with physical disability did not have the opportunity to use high-tech AT related to mobility and education such as an electric wheelchair or adapted keyboard. Students with intellectual disability did not have the opportunity to use high-tech AT such as a mobile phone, which related to communication. Students with hearing disability did not have the opportunity to use high-tech AT such as an FM system, which also related to communication. Lastly, students with visual disability did not have the opportunity to use high-tech AT such as a Thermoform Brailon Duplicator, which related to communication as well. This information reflected that some needs for high-tech AT items were not reached by children with disabilities, although they were addressed in the rights of students with disabilities under ministerial regulations. This was due to a problem with management policy and national socio-economic status. Most high-tech AT items are expensive and so many steps have to be taken before receiving them. In addition, the AT items addressed in ministerial regulations are provided only for students who studied in mainstreaming schools and are in the Individual Education Plan (IEP). On the other hand, most students with disabilities are placed in special education schools in Thailand and some have no IEP. These problems are barriers against AT provision and interesting points for further study. This study found that the physical environment, in terms of the learning environment, is a facilitator for all types of students with disabilities. This is because this study researched in special education schools that were designed for specific disabilities and suitable for limiting each type of disability. These schools are based on a universal design concept in order to encourage, particularly students with physical disability, to be able to access classrooms, because the way buildings are constructed has direct impact on student participation [17]. In addition, these findings indicated that the physical environment is as important as the social one. In fact, besides the physical environment, the lack of understanding and positive perspectives in society leads to barriers against improving the occupational performance of students with special needs [18]. When these students have a good relationship with their teachers, peers, other students, and other school staff, they will be motivated to improve their learning and increase their opportunity to participate in school activities [19,20]. The findings in terms of the learning environment indicated that students and adults perceived both the physical and social environment as facilitators in special education schools by responding to the questionnaire. It is of interest that as some of the students in the physical environment need facilitators, others see them as barriers, and special education schools do not need the same building construction plans. Instead, they should construct with a barrier-free design for each type of disability. Furthermore, the social environment, including relationships with teachers, peers, other students, and other school staff, is important for all of the students. In providing information to build understanding and a positive attitude in school societies, facilitators encourage children with disabilities to participate in school and social activities in the same way as people do in general.
Previous researches surveyed the AT items that students with disabilities needed, used, and had problems using. This study provides more findings in terms of student perspectives on AT and the learning environment as facilitators or barriers. Moreover, this study reflected findings on the opportunities for students with disabilities to receive high-tech AT in provincial areas of a developing country. In addition, this study is useful for related service providers such as educators, school therapists, and administrators in that it examines the individual needs of each student and presents the AT that will meet them. These people have to be aware of a safe design for AT together with environmental evaluation and encouragement in understanding students with disabilities, in order to relate with people and build relationships in the school context. The limitation of this study was the specific area of Chiang Mai province. Further research should expand to wider areas and study mainstreaming schools that include various types of students with disabilities in the same school. Furthermore, the roles of school-based occupational therapists should cover devices and environmental adaptation, and studies on their effectiveness would be interesting for students with disabilities. In addition, further studies might research other aspects and their correlation with, for example, physical environment and mobility, social environment and communication, or learning environment and education.

Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
The authors declare no conflict of interest.


s2fieldsofstudy: Education Linguistics

text:
Task-Based Language Teaching: Affordances and Challenges in TBLT Implementation at the Vietnamese Tertiary Level

Task-based language teaching (TBLT) has been increasingly adopted and has emerged as a central concept from curriculum guidelines and syllabi (Nunan, 2003) in the Vietnamese context. Although TBLT has been vigorously and explicitly promoted by recent language education policies in Vietnam (Nguyen, 2011), a variety of affordances and challenges have arisen in connection with the implementation of TBLT in Vietnamese EFL classrooms. This report explores different factors that afford opportunities for or hinder the implementation of TBLT in the Vietnamese tertiary context, where students often lack the chance to practice the target language beyond the classroom doors.

Introduction
Task-based language teaching (TBLT) has been increasingly adopted and has emerged as a central concept from curriculum guidelines and syllabi (Nunan, 2003) in the Vietnamese context. Although TBLT has been vigorously and explicitly promoted by recent language education policies in Vietnam (Nguyen, 2011), a variety of affordances and challenges have arisen in connection with the implementation of TBLT in Vietnamese EFL classrooms. This report explores different factors that afford opportunities for or hinder the implementation of TBLT in the Vietnamese tertiary context, where students often lack the chance to practice the target language beyond the classroom doors.

Affordances and Challenges in TBLT implementation
Within various interpretations of TBLT, significant features of this approach are emphasized. First, TBLT is defined as "learner-centered approach to language teaching" (Van den Branden, 2016, p. 164) which aims to 'develop learners' communicative competence by engaging them in meaning-focused communication through the performance of tasks" (Ellis & Shintani, 2014, p. 135). Second, it also develops learners' linguistic and interactional competence (Ellis & Shintani, 2014). In short, a fundamental principle of TBLT is that although learners are mainly concerned with comprehending and constructing messages, they are also required to attend to form for learning to occur (Ellis & Shintani, 2014).
Over the past decades, the Vietnamese government has shifted national language policies and curricula towards different versions of communicative language teaching including TBLT (Yen, 2016) to foster the quality of EFL teaching and learning in the Vietnamese context. On the national level, English language competence is seen as human capital to help boost the countries' economic competitiveness in the global market. In the particular context of Vietnam, an English language education initiative, known as Project 2020, has recently been launched by the government to enhance the quality of English language teaching and learning at all educational levels in the national education system. An objective of the project is to enable university students to become effective English language users who can function successfully in different environments (Le & Do, 2012). Specifically, Vietnamese undergraduates should communicate in English proficiently at B1 the (CEFR) level. In line with the national policy, there has been a growing tendency for English to be adopted both as the medium of instruction (Vu & Burns, 2014) and a compulsory subject through task-based teaching being the principal teaching method (Hoang, Nguyen, & Hoang, 2006) in many universities across Vietnam. This means that task-based approaches are adopted at all levels of the curricula (Adams & Newton, 2009). Thus, TBLT has been advocated by both the policymakers and curriculum initiatives in Vietnamese tertiary education. Specifically, the government of Vietnam supports the organization of workshops and conferences for teachers on TBLT (Le, 2011). As a result, Vietnamese lecturers and teachers of English have begun to apply TBLT in their classrooms and achieved a particular outcome (e.g., Nguyen, Newton, & Crabbe, 2015;Yen, Van den Branden, Van Steendam, & Sercu, 2015). However, studies have also revealed a mismatch between the core features of TBLT and local pedagogical practices (e.g., Barnard & Viet, 2010;Nguyen, Le, & Barnard, 2015;Nguyen, Newton, & Crabbe, 2015;Yen, 2016). This is because of diverse factors hindering the adoption of TBLT in the actual practices.

Institutional Factors
The primary barrier to adopting TBLT in the Vietnamese tertiary setting is the institutional factors, which are considered a psychological burden for EFL teachers to apply TBLT in their classrooms. The form-focused examination system in most universities throughout Vietnam focuses largely on a summative assessment of multiple choice testing formats, which may result in teachers' concentrating on giving explicit grammar instruction rather than focusing on meaning, which is argued to be an important feature of tasks (Ellis, 2003). Moreover, the use of commercial textbooks, such as New English Files and Cutting Edge in most universities including my institution, does not support task-based instruction (Le, 2008). Many activities in those textbooks neither reflect "a work plan for student activity" nor "seek to achieve an interactional authenticity" (Ellis, 2003, p. 8). Teachers may also be required to stick to a syllabus that specifies what is to be achieved regarding vocabulary and grammar structures. Consequently, they tend to base their teaching on the items available rather than on tasks, which have an indirect relationship to the linguistic knowledge of the syllabus (Ellis, 2011). Another contributing factor to the institutional constraints is the lecturers' heavy workload required by the universities (Pham, 2001). A majority of lecturers work like a teaching machine (Pham, 2001), a "tool provider," a metaphor mentioned by Farrell (2015, p. 62). As a consequence, teachers have little time to keep themselves abreast with current TBLT-related knowledge. For instance, many English lessons have been delivered exclusively through a grammar-based approach that mainly focuses on accuracy and testing (Pham, 2001), which may limit the adoption of TBLT. Adams and Newton (2009) asserted that schools adopting TBLT need to create a supporting environment for teachers to carry out experiments with new teaching approaches in their classrooms. However, the current negative washback effects of examinations, the adoption of commercial textbooks without adaptation and teachers' heavy schedules act as challenges for the TBLT to be implemented in Vietnamese tertiary settings.

Classroom Factors
The second set of difficulties with implementing TBLT in Vietnamese tertiary context stems from classroom factors. Such constraints include what Butler (2011) mentioned in his study, which are the large class sizes with students of different proficiency levels, issues with classroom management and limited numbers of English instructional hours (Butler, 2011). As Littlewood (2007) argued, it is challenging to implement TBLT in large classes since students are noisily engaging in group discussions (Carless, 2012), which may lead to other teachers' complaining. Butler (2011) also added that it is demanding for teachers to employ alternative activities in large classes to ensure that every student participates. Moreover, students' multi-level proficiency reflects a challenge in terms of teachers' selecting, designing and organizing appropriate tasks (Adams, 2009). Vietnamese tertiary classrooms themselves also act as a

Phuong Thi Hong Cao
The Journal of Asia TEFL Vol. 15, No. 2, Summer 2018, 510-515 hindrance for task-based lessons because of the rigid set of rows of chairs and desks. In the last five years, I have been in charge of teaching different classes comprising of more than 40 undergraduates of multilevel proficiency in classrooms where desks are tightly arranged in four rows. I have found it hard to employ appropriate classroom management techniques and suitable activities to promote task-based learning. In respect to the limited number of teaching hours in the Vietnamese tertiary setting, English instruction is only allocated between three and four hours per week in most universities nationwide (Vu & Burns, 2014). There is also teachers' pressure of completing the syllabus (Carless, 2003), which impacts on the time available to conduct activities and tasks (Carless, 2003) through TBLT. In short, the reality of classroom factors mentioned above has made any methodological innovation, including TBLT, difficult to implement.

Teachers' Perceptions
Last but not least, teachers' perceptions of core features of TBLT reflect a potential challenge in the adoption of a strong version of TBLT, as teachers' conceptions and beliefs about pedagogy are more influential (Carless, 2012) than other factors affecting the success of any educational innovations. In the Vietnamese tertiary context, knowledge about TBLT may have been accessible to teachers in teachers' manuals, conference materials, and implicitly embedded in the textbooks. However, teachers might only appear to adjust their attitudes (Viet, 2014). Consequently, many lecturers conduct their lesson plans with activities organized in a form-focused sequence, reflecting a weak form of TBLT, namely, the Presentation -Practice-Production (PPP) model (Viet, 2014), which is similar to what Carless (2009) found in Hong Kong contexts. This practice is not aligned with that advocated by a TBLT framework as proposed by Willis (2005). For example, research by Nguyen, Le and Barnard (2015) shows that Vietnamese teachers' understanding of TBLT was somewhat limited, with the teachers tending to focus on forms rather than on meaning. They believe that "the students needed an explicit knowledge of relevant grammatical features before they could communicate effectively" (Nguyen, Le, & Barnard, 2015, p. 79). It seems that Vietnamese teachers share the same difficulty as their colleagues in other Asian EFL contexts as mentioned by Carless (2012) regarding receiving "sufficiently in-depth teacher education to illuminate the key issues in implementing TBLT successfully" (Carless, 2012, p. 353). This lack of training also affects their perceptions of interpretation of the TBLT framework and the nature of tasks. In sum, teachers in different contexts including the Vietnamese tertiary setting, as mentioned by Adams and Newton (2009) "have not had the opportunities to gain sufficient understanding of tasks and task-based teaching" (Adams & Newton, 2009, p. 9), so that they can adopt it in their classrooms. A vague understanding of TBLT may lead to their confusions of the nature of TBLT and its implementation in the actual practices.

Implications and Recommendations
As previously mentioned there are both opportunities and potential barriers to the implementation of TBLT in the Vietnamese tertiary classrooms. This report has attempted to discuss possible recommendations to address the issues. As argued by Van den Branden (2006), ambitious educational policies can only be successful if sustained efforts are made by by different stakeholders. As clearly stated, from the national level, the Vietnamese language policy explicitly promotes the implementation of TBLT in all educational settings, including the tertiary context. However, an efficient ELT policy needs to consider the contextual factors (Cao, Ta, & Hoang, 2016), in which the actual teaching practices occur. Thus, the policy is more likely to achieve its ultimate goals.
The primary recommendation with respect to the implementation of TBLT is how to create an appropriate task-based environment from institutional perspectives. First, "communicative tasks and taskbased assessments" (Butler, 2011, p. 46) should be employed instead of classic form-focused examination,

Phuong Thi Hong Cao
The Journal of Asia TEFL Vol. 15, No. 2, Summer 2018, 510-515 which promotes teachers to utilize formative assessment types such as project work, presentations and other alternative assessments. Second, university authorities should encourage teachers and curriculum developers to design or adapt textbooks towards more task-like activities, which are argued to target the students' real world interest (McDonough & Chaikitmongkol, 2007). A successful case has been shown in a similar context, at Chang Mai University, in Northern Thailand, where the English department faculty reframed the teaching syllabus into one that promoted a TBLT perspective and conducted a pilot program before introducing it university-wide. The success of this case regarding increased learner independence and real-world tasks would be a possible implication for other cases in South East Asia to consider.
Another recommendation regarding the classroom factors is that institutional policymakers should consider reducing the number of students in English classes to make them more feasible and appropriate for classroom interaction opportunities to occur. Teachers who are directly dealing with large class size issues should establish a code of behaviour in their classrooms to reduce the noise level. A more appropriate classroom environment to TBLT can satisfy three essential conditions as mentioned by Willis (2005). These are "the provision of exposure to the target language; the provision of opportunities to use the target language for real communication; and the provision of motivation for learners to engage in the learning process" (Willis, 2005, p. 19).
Since teachers are considered as the mediators and agents of change (Van den Branden, 2016) who drive the process of TBLT implementation forward, they play a crucial role in planning and designing lesson activities that "are tailored to students' internal syllabi" (Van den Branden, 2016, p. 179). Thus, to allow Vietnamese lecturers to obtain confidence and develop the professional expertise necessary for TBLT work, professional support regarding TBLT knowledge should be provided for teachers from TBLT experts through intensive training and coaching (Van den Branden, 2016). My institutional concern is similar to that described by McDonough and Chaikitmongkol (2007) in terms of teachers' needs of knowledge and skills of principle and philosophy of TBLT. With this support, teachers will be able to experiment, reflect, and revise task-based activities or a TBLT syllabus. By doing this, they can develop these skills sustainably and independently to make a radical transition from "traditional L2 teaching methods to TBLT" (McDonough & Chaikitmongkol, 2007, p. 124).

Conclusion
Although the educational policy in Vietnam has firmly supported the TBLT approach, it seems that the implementation of TBLT from such policy into actual classroom practice in the Vietnamese tertiary context has faced many difficulties, including institutional, classroom and teachers' constraints. Van den Branden (2006) asserts that TBLT takes a long time to become fully incorporated into classroom practice. The incorporation will be more likely to succeed if differently related stakeholders (Van den Branden, 2006) such as policymakers, school authorities, syllabus developers, in-service trainers, and practitioners can play their active parts in operating along agreed principles to optimize the opportunities for TBLT to fulfill its tasks.
In conclusion, adopting TBLT is not just a question of mastering a new teaching approach, it is more a matter of perspective and sensitive management of the learning environment (Willis, 2005). Considering myself as a reflective practitioner who has just established a task-based cycle for the first time in the Vietnamese tertiary classrooms, the most significant challenge for me is "processing the strength of mind to stand back with confidence, and to let learners get on with their own learning" (Willis, 2005, p. 148). With this in mind, I firmly believe that I will commit myself to participating in this promising TBLT endeavor.

The Author
Phuong CAO (Cao Thi Hong Phuong) currently works as a lecturer and teacher trainer at the faculty of English, Hanoi National University of Education, Vietnam. She received her M.A degree in TESOL from School of Linguistics and Applied Language Studies, Victoria University of Wellington (VUW). Her research interests include strategic learning; teaching and learning vocabulary, ELT methodology and language teacher education. She has regularly presented her research papers at both national and international TEFL and TESOL conferences.


extfieldsofstudy:
Computer Science
Physics

text:
Scientific collaborations within a university: From the viewpoint of complex networks

While the topological features of scientific collaboration networks have been well explored in literature, the mechanisms by which the researchers are assembled remain not very clear. Here, by a 6-year publication data of the Shaanxi Normal University (SNNU), we construct the collaboration network and, combining with the additional information of the research staffs obtained through survey and interviews (including department affiliation, research direction, seniority, acquaintanceship, etc), investigate the key factors that promote scientific collaborations. It is found that, although of a relatively small size, the constructed network possesses still the general features of large-scale complex networks, including the community structures, the heterogenous degree and weight distributions, the preferential attachment of the new links, etc. In exploring the assembly mechanisms of the collaboration patterns, it is further revealed that among the many factors influencing scientific collaborations, the closeness of the research directions has a clear advantage over the others. The findings provide insights into the formation of the collaboration patterns within universities, and might helpful to the network modeling and research management in practice.

Introduction
Bibliometric studies over the past two decades have shown a rapid increase in the number of coauthored papers in every scientific discipline, as well as within and across different countries and geographic areas. Indeed, with the increased complexity of the scientific problems (e.g., the brain research) and experimental facilities (e.g., the Large Hadron Collider), the demanding for largescale, interdisciplinary scientific collaborations is more urgent than ever [1,2,3]. To understand the patterns of scientific collaboration and their impacts on knowledge production and discipline evolution, scientific collaboration itself has became an active research topic in the past decades [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18], and has attracted considerable attentions in a variety of disciplines such as information science, management science, psychology, sociology, philosophy, to name just a few. Recently, stimulated by the significant progresses achieved in network science [19,20,21], a new powerful method, namely the collaboration (co-authorship) complex network, has been proposed and employed in analyzing the scientific collaborations [4,5]. In the general collaboration networks, authors are represented by nodes, and two authors are connected by a link if they have co-authored a paper. Thanks to the well-recorded electronic bibliographic datasets, large-scale collaboration networks have been reliably constructed and systematically investigated for a variety of disciplines in recent years, in which many novel properties of scientific collaborations have been disclosed, e.g., the small-world and scale-free

Topological properties
The bibliometric dataset used in the present work is based on the journal publications recorded by the Institute for Scientific Information (ISI) during the period from January 2009 to December 2014. To focus on only the high-quality collaborations among research staffs working on natural science within SNNU, we filter the bibliometric dataset by the following requirements: 1) SNNU is appeared as the first address; 2) The paper is published in journals of natural science; 3) The journals are listed in the 1st and 2nd divisions of the Journal Citation Reports [28]; and 4) The paper is co-authored by at least two staffs from SNNU (i.e., excluding the solo publications and papers co-authored by the supervisor and his/her students). After such a filtering, there are totally 1645 papers left in the dataset, which are collaborated by N = 503 staffs distributed in M = 16 different departments in the university. (During the investigated period, the journal list is slightly modified year by year. In obtaining the dataset, we include a journal into the list once it appears in the 1st or 2nd divisions during the period.) Having obtained the bibliometric dataset, we next construct the scientific collaboration network following the conventional method of network construction [4,5]. In constructing the collaboration network, we represent each staff by a node, and two staffs are connected by a link if they have co-authored a paper. We have in total L = 935 links in the network. The weight of the link, w ij , is defined as the number of co-authored papers between staffs i and j. The nodes are colored according to the departments, i.e., staffs affiliated with the same department are of the same color. The topological structure of the collaboration network is presented in Fig. 1. Unlike the general complex networks where nodes are reachable to each other (e.g., the Internet) [19,20], here it is seen that the collaboration network is fragmental and scattering -a feature commonly observed in scientific collaboration networks [11,25]. In particular, the network is constituted by a giant cluster and many small motifs. The giant cluster contains 401 nodes and 842 links, with staffs coming from 12 different departments. The disconnected motifs are of various sizes, ranging from 2 to 11. The node size is plotted as proportional to the node degree, and the edge thickness is plotted as proportional to the link weight (the number of co-authored papers). Nodes are colored according to the departmental information of the staffs. The network is made up of a giant cluster (which contains 401 nodes and 842 links), 12 small motifs (of size between 3 and 11), and 17 nodepairs. (b) The degree distribution follows roughly the power-law scaling, p(k) ∝ k γ , with the fitted exponent γ ≈ −1.8. (c) The weight distribution follows roughly the power-law scaling, p(w) ∝ w α , with the fitted exponent α ≈ −1.6. (d) The variation of the average-nearestneighbor-degree, k nn , as a function of k, with the fitted slope β ≈ 1.4 × 10 −2 .
We go on to explore the topological properties of the network. Previous studies have shown that realistic complex networks normally possess the small-world and scale-free features [22]. As the network is fragmental, in investigating the small-world feature we focus on only the giant cluster. The numerical results show that the giant cluster has the averaged diameter d ≈ 6.4 and the clustering coefficient c ≈ 0.55. Clearly, the giant cluster possesses the small-world feature. To investigate the scale-free feature of the network, we plot in Fig. 1(b) the distribution of the node degrees, k. It is seen that the degrees are of heterogenous distribution. Particularly, the degree distribution follows roughly a power-law scaling within the range k ∈ [1, 38], i.e., p(k) ∝ k γ , with the fitted exponent γ ≈ −1.8. Besides the node degrees, the distribution of the link weights is also heterogenous, as depicted in Fig. 1(c). Specifically, the weight distribution follows roughly the power-law scaling p(w) ∝ w α within the range w ∈ [1,90], with the fitted exponent α ≈ −1.6. For scientific collaboration networks, another commonly interested topological property is assortativity (assortative mixing), i.e., the propensity that nodes of the similar degrees are connected to each other [23]. To explore this property, we measure the average-nearest-neighbor-degree, k nn , for node of degree k in the network, and plot the variation of k nn as a function of k [29]. If k nn is increasing (decreasing) with k, then the network is classified as assortative (disassortative) [23]. The results are presented in Fig. 1

(d).
It is seen that, with the increase of k, the value of k nn is fluctuating wildly, showing no clear trend of increase or decrease. (By a linear fitting of the data, in fact we have k nn ∝ βk, with the slope β ≈ 1.4 × 10 −2 .) Fig. 1(d) thus indicates that the collaboration network of SNNU has nearly the neutral assortativity, which is different from the general collaboration networks studied in literature (which are normally assortative) [23,29]. The neutral assortativity might be attributed to the limited size (resulting in the large fluctuations) and sparse connections (resulting in the fragmental structure) of the SNNU collaboration network.
In investigating scientific collaboration networks, a particularly interested topological property is the community structure [30], i.e., the network is partitioned into some communities, with the connectivity inside each community is much denser than between the communities. As community reflects the meso-scale structure of complex networks, it has been employed as an important approach to exploring the patterns of scientific collaborations in previous studies [4,11,15]. Here we choose the giant connective cluster in the network, and partition it into communities according to the algorithm proposed by Girvan and Newman [30]. In particular, we remove recursively the network links by a descending order of their betweenness, and monitor the variation of the network modularity, Q. [Roughly speaking, modularity measures the probability for a pair of connected nodes randomly chosen to be staying within the same community, which is varying between 0 (no community structure) and 1 (distinct community structures). Please refer Ref. [30] for the detail definition of Q]. By finding the maximum modularity, Q max , we are able to identify the optimal partition of the communities. Numerically, we find that Q max ≈ 0.9, under which the network is partitioned into M = 21 communities, with the size of the communities ranging from 3 to 62.
Do the identified communities reflect the realistic situation? In partitioning the staffs within a university into communities [30], a conventional approach in social science is to group the staffs according to their departmental affiliations. That is, staffs from the same department form a community. Apparently, the two types of communities, i.e., the collaboration and department communities, are different from each other. For instance, the giant cluster contains 21 communities, while the staffs are from only 12 departments. However, as scientific collaborations between researchers are largely influenced by their disciplines and acquaintanceship, the two types of communities do have certain degree of correlations. To quantify the correlation between the two types of communities, we introduce the community-correlation coefficient (CDC) with n l the size of the lth collaboration community identified through the Girvan-Newman algorithm, and n l the maximum number of nodes affiliated to the same department within this community. . . . represents that the result is averaged over all M communities. Clearly, the larger r is, the stronger the correlation between the two types of communities is, and the stronger the community structures influenced by the departmental affiliations will be. The calculated result shows that r ≈ 0.8. This strong correlation is somewhat expectable, as scientific collaborations within the same discipline in general have the higher propensity than cross disciplines, and, more importantly, staffs within the same department are usually acquaintances and friends [1,2]. We would like to note that, while staffs within the same department are more likely to collaborate with each other (as compared with the collaborations between staffs from different departments), the collaborations are not of the equal probability, i.e., there exist some collaboration patterns within the department. For instance, staffs in the physics department are organized into 4 communities. Our above analysis of community structures thus suggests that departmental affiliation is influential, but not crucial to the scientific collaborations.

Assembly mechanisms
To explore the key factors promoting scientific collaborations, we next study the collaboration patterns at the nodal level, based on the additional information of the staffs obtained through the conventional approaches of survey and interview. Specifically, we are going to analyze in detail: 1) the collaboration propensity of the newcomers, i.e., with whom the newcomers tend to collaborate; 2) the collaboration patterns within three specific, small-size departments; and 3) the collaboration pattern at the departmental level within SNNU.
In modeling the growth of collaboration networks, it has been commonly assumed that the new nodes (newcomers) are connected to the existing nodes (veterans) by the mechanism of preferential attachment [6,9,10,16]. That is, the larger the degree of an existing node is, the higher the possibility for it to be connected by the newcomer will be. While preferential link attachment has been observed in the empirical data of many natural and man-made complex networks [19], it remains unknown whether it applies to the growth of scientific collaboration network, as the relevant data is unavailable from the electronic bibliographic datasets. This suspicion is reasonable, as it usually takes a certain period for a newcomer to familiarize the new academic environment and find the new collaborators, resulting in probably the non-preferential link attachment. According to the surveyed information, we identify from the dataset the veterans who joined SNNU before 2009, and plot in Fig. 2 the average number of newcomers (staffs joined SNNU between 2009 and 2014) they have collaborated with, n k , versus their degrees, k. It is seen that, in accordance with the assumptions made in previous modeling studies, the value of n k is indeed increasing with k by roughly a linear fashion. Fig. 2 provides (probably the first) empirical evidence showing the applicability of preferential attachment mechanism in modeling scientific collaboration networks. We continue to analyze the collaboration patterns within a specific department. Due to the limited resources, we have only surveyed three research-oriented departments inside SNNU, namely the departments "A", "B", and "C". Department "A" is a national engineering laboratory, which was founded in 2008 and now has 41 incumbent staffs. According to our dataset, department 'A" has totally published 96 co-authored papers, which involve N A = 35 staffs and give L A = 54 collaboration links. The topological structure of the collaboration network for department "A" is presented in Fig. 3(a). It is seen that the nodes are organized into roughly a star-structure. Specifically, there is a supper (central) node of degree k = 22 in the network, which contributes 61% of the total publications; while the degrees of the remaining (peripheral) nodes are less than k = 6. From the surveyed result, it is also learned that the super "node" is a senior researcher of broad research interest, with research directions covering almost all the directions of the peripheral "nodes". Besides the star-structure component, there also exists an isolated node-pair and two isolated nodes in the network. By checking the research background of the these staffs, it is revealed that the these staffs are working on a research directions not covered by the super "node".
Department "B" is a key laboratory of the Ministry of Education of China, which was founded in 2011 and now has 21 incumbent staffs. During the investigated period, N B = 17 staffs have co-authored 39 papers in total, giving L B = 26 collaboration links. The topological structure of the collaboration network for department "B" is presented in Fig. 3(b). It is seen that, different from department "A", the collaboration network of department "B" consists of 2 disconnected clusters, together with 5 isolated nodes. Comparing to department "A", it is seen that the node degrees of department "B" are more homogenously distributed, with k ranging from 1 to 7. By interviewing with staffs within department "B", it is learned that this is an interdisciplinary department founded by integrating three institutes from different departments. Although staffs in department "B" have the same research interest, they are actually working on different research directions and topics. Fig. 3(b) thus suggests that departmental affiliation is not the necessary condition for promoting scientific collaborations.
Department "C" is a provincial key laboratory, which was founded in 2005 and now has 36 incumbent staffs. During the investigated period, N C = 34 staffs have co-authored 139 papers in total, giving L C = 54 collaboration links. The topological structure of the collaboration network for department "C" is presented in Fig. 3(c). In comparison with the networks of departments A and B [ Fig. 3(a) and (b)], the network of department C is featured by the existence of a few of tightly connected hub nodes. More specifically, the four largest-degree nodes [of node indices 14 (k = 12), 15 (k = 9), 17 (k = 7), and 22 (k = 6) in Fig. 3(c)] are globally connected and form the core of the community, to which most of the remaining nodes can reach by only one link. By interviewing the staffs, it is learned that this laboratory has long been established (back to 1980s) before promoting to the provincial key laboratory, and the research interest of the staffs are highly correlated. In particular, the four hub "nods" are of the same research direction, but working on different topics.
Our above analysis on the formation of collaboration patterns points to the fact that, among the many influenceable factors, research direction has the dominating role in affecting scientific collaborations. More specifically, the closer the research directions between two staffs is, the higher the possibility for them to collaborate with each other will be. This is evidenced in the collaboration patterns of department "A" [Fig. 3(a), where the research directions of the peripheral "nodes" are different from each other, but are all overlapped with the super "node"], department "B" [Fig. 3(b), where the research directions for nodes within the same cluster is highly centralized, but are different among the clusters], and department "C" [Fig. 3(c), where all "nodes" are working on different topics of the same research direction].
To explore further the crucial role of research direction on collaboration, we finally explore the collaboration patterns at the department level, i.e., combining nodes of the same color into a new node and integrating the links accordingly. In such a way, the original network is compressed into a small-size network of only M = 16 nodes and L = 16 weighted links. (For a better presentation, only links of weight larger than 5 are kept). The network structure is presented in Fig. 3(d). Several features are observed in this departmental collaboration network, which manifests from another aspect the importance of research direction on scientific collaborations. Firstly, despite the large number of recorded publications, the network is still not globally connected, implying the difficulty of interdisciplinary collaborations (resembling the feature of department "B"). Secondly, the hub (non-hub) nodes stand for departments of fundamental (modern) science, e.g., the largest-degree (k 1 = 7) and second largest-degree (k 2 = 6) nodes symbolize, respectively, the chemistry (node 1) and biology (node 2) departments. This feature is similar to that observed in department "A", where the super "node" is of broad research interest and the peripheral nodes are of focused research interest. Thirdly, the connection between nodes 1 (the chemistry department) and 5 (the department of material science) is extremely strong (totally 95 co-authored papers), despite the fact that they belong to different disciplines. By checking the history of these two departments, it is found that node 5 was separated from node 1 in 2011, and many staffs still continue their collaborations since then. This finding, together with the finding of CDC in Sec. II, confirms the fact that scientific collaborations are influenced but not governed by the department affiliations.

Discussion and conclusion
The present work is featured by considering additional information about the network nodes and links. While collaboration networks have been extensively investigated in literature, the existing studies are mostly focusing on the network topological properties (e.g., degree distribution, small-world effect, and community structures) and less attention has been paid to the detailed information of the network nodes and links (e.g., affiliation, seniority, research direction, and acquaintanceship). In the present work, we pick up these missed information by the conventional methods used in social science (i.e., survey and interview), and revisit the assembling mechanism of collaboration patterns. With the additional information, we are able to analyze individually the factors that affect collaboration and, more importantly, identify the key elements, providing therefore a deeper understanding on the mechanism of scientific collaboration. We would like to note that all the findings are based on the specific publication dataset of SNNU, and it remains unknown to us whether the same results stand for other universities and organizations. In our further works, we shall keep on updating the SNNU publication dataset (e.g., extending the publication period), and, in the meantime, try to justify our findings by more datasets from other universities national-and world-wide. It is our hope that the findings, and also the new approach of collaboration analysis proposed in the present work, would draw attention from researchers in the relevant fields, and, by "collaborating" with each other, we together could unveil the origin of scientific collaboration inside universities. (All data used in the present work can be obtained from the authors upon request for research purposes.) Summarizing up, based on a 6-year publication dataset of SNNU, we have constructed the scientific collaboration network of the university, and, combining with the staff additional information obtained through survey and interviews, explored in detail the assembly mechanism of various collaboration patterns. It is found that, among the many factors influencing scientific collaboration, the closeness of the research directions has a clear advantage over the others. The finding sheds new light on the mechanism of scientific collaborations, and might helpful to the policymaking and management of research activities in universities.


Technological Pedagogical Content Knowledge of Prospective Mathematics Teacher in Three Dimensional Material Based on Sex Differences

The effectiveness of learning in this era can be seen from 3 factors such as: technology, content, and pedagogy that covered in Technological Pedagogical Content Knowledge (TPCK). This research was a qualitative research which aimed to describe each domain from TPCK include Content Knowledge, Pedagogical Knowledge, Pedagogical Content Knowledge, Technological Knowledge, Technological Content Knowledge, Technological Pedagogical Knowledge and Technological, Pedagogical, and Content Knowledge. The subjects of this research were male and female mathematics college students at least 5th semester who has almost the same ability for some course like innovative learning, innovative learning II, school mathematics I, school mathematics II, computer applications and instructional media. Research began by spreading the questionnaire of subject then continued with the assignment and interview. The obtained data was validated by time triangulation.This research has result that male and female prospective teacher was relatively same for Content Knowledge and Pedagogical Knowledge domain. While it was difference in the Technological Knowledge domain. The difference in this domain certainly has an impact on other domains that has technology components on it. Although it can be minimized by familiarizing the technology.

Introduction
The age of globalization has an impact on everything such as communications, informations and tools that more advanced from the past [1]. This also affects the learning. Learning that was originally using traditional learning system must became a modern learning system or in other words learning must using technology that develops today. In learning teacher should have extensive knowledge of the technology that to be taught, pedagogy skills in classroom management based on technology, and technical knowledge about technology that to be used [2]. In general there are three main factors that affectthe effectiveness of learning with technology such as:technology, pedagogy, and materials that to be taught. A knowledge framework that links these three factors in complexly called Technological Pedagogical Content Knowledge (TPCK) [3].
There are seven knowledge domains from TPCK which is a combination of the three bodies components such as: content, pedagogy, and knowledge. The seven knowledge domain are Content Knowledge (CK), Pedagogical Knowledge (PK), Technological Knowledge (TK), Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), and Technology, Pedagogy and Content Knowledge (TPaCK) [3]. CK is a knowledge about learning materials that to be taught, in this research is math lessons include facts, concepts, principles, and rules [3,4]. PK is knowledge of methods and processes in learning include knowledge about classroom management, assessment, and lesson plans [3,4] of the learning process in terms of the material that to be taught [3,4]. TK is knowledge of a variety in technologies, from low -tech technology to digital technology [3,4]. TCK is the knowledge of how technology can provide a new representation from a particular subject, in this research is math lesson [3,4]. TPK Is knowledge about how various technologies can be used in the learning process [3,4]. TPaCK is the knowledge needed by teachers to integrate technology in the learning process for each subject matter [3,4]. Description of each domains knowledge from TPCK and relationship among them can be seen in Figure1.
In learning, teacher must know the various domains of knowledge contained in TPCK and know the relationship between these domains. Many teachers experience a lack of preparation in using today's technology because they earn a bachelor's degree when technology is more simple than today [3]. This leads prospective teacher who will become teacher have a big role in learning with technology.
That because technology will continue to evolve over time. Mathematics is one example of learning that technology's necessary needed. Technology can become the bridge of abstract thinking in learning mathematics. The three dimensional material is one of the mathematical content area which it called space & shape [5].
One of relevant study is from Jordan entitled "The influence of gender on beginning teachers' perceptions of their Technological Pedagogical Content Knowledge" which results there are significant differences between male and female beginner teachers. Females rated their knowledge higher than males in one domain that is Pedagogy Knowledge and man rated their knowledge higher than female in six other domains [6]. Data retrieval is done by using closed questionnaire only, so the data obtained only in form of perception and can't describe completely. From that matter, research about TPCK based on sex differences that use qualitative approach in order to get the whole description is urgently needed.

Research methods
This research is qualitative descriptive research. Qualitative approach is used so that it produces descriptive data in the form of written or oral words from people and observed behavior. The subjects of this research were mathematics college students at least 5th semester that already thought some course like innovative learning, innovative learning II, school mathematics I, school mathematics II, computer applications and instructional media.The subjects consist of two college students selected based on the criteria that the two students have nearly equal ability and have different sex. Research began by spreading the questionnaire of subject then continued with the assignment and interview.
The instrument in this research is task-based interview guide. The task-based interview guide is used as a directive in the interview to obtain the information that is needed. Task in this research is to create lesson plans with help of technology on three dimensional materials. Meanwhile, the type of interview used in this research is semi-structured that it contains about extracting more information about the task undertaken by college student. The interview was conducted after the college students made the lesson plansbased on technology. After the data obtained, validation of the data is using time triangulation with breaks time about three weeks. Data analysis in this research are data reduction, data presentation, and conclusion [7]. In data reduction, data based on interview is coded according to the indicators, selecting and disposing of unnecessary information. Then the data is classified according to each TPCK domains. In the data presentation, there are a collection of information that arranged systematically so it can be drawn a conclusion. In conclusion, the researcher gives an overview for each domain in TPCK.

Results and Discussion
The description of each TPCK domains for prospective teacher based on sex differences are presented in Table 1. Based on Table 1,it describe seven domains of TPCK include CK, PK, TK, PCK, TCK, TPK and TPaCK. It shows that there are similarities and differences between male and female prospective teacher. Based on obtained data, show that for CK and PK domains almost has no differences. For the PCK domain, both male and female prospective teacher is much alike in selection of models, methods, approaches, and related to each other. Slightly differentiate that male students are more concerned with the suitability of the material that to be taught while the female prospective teacher focus more on the suitability of the applicable curriculum.
The big difference between male and female prospective teacher was found in TK domain. This is due to significant differences in terms of gender-related attitudes to ICT, ICT skills, and use of ICT [8]. Male prospective teacher knew various types of applications but limited when using it where female prospective teacher knew the various applications but felt difficult when using it. Here it is seen that male prosepctive teacher are indirectly more able to use computer applications than female prosepctive teacher. Female prosepctive teacher have difficulty in using computer applications, unlike male prosepctive teacher although their use is still limited in basics. This is because both men and female have significant differences in ICT skills with male have higherscores than female [9]. Differences in TK domain certainly have an impact on other domains that have technological components such as TCK, TPK, and TPaCK.
In TCK domain, male prospective teacher can represent concept of the three dimensional material according to the technology that used while the female prospective teacher are less able to apply the representation of facts, concepts, principles and rules on the three dimensional according to the technology that used. This is certainly not separated from the technology that they used, geogebra selected by male prospective teacher is easy in represent the concept compared to technology namely power point that used by female prospective teacher [2].
In TPK domain, male prospective teacher choose technology that is effectively and efficiently in terms of learning design while female prospective teacher choose technology that is practical and easy to use. In contrast to male prospective teacher, female prospective teacher feel unable to use geogebra so that she used power point which in fact it is the application that she mastered. There are an indication that female teachers have low self-confidence in using ICT in learning compared to male teachers [10]. The same is also on TPaCK domain where male prospective teacher utilize technology as a reinforcement concept in the learning of the three dimensional material while the female prospective teacher utilize technology only as the presentation of the material in learning three dimension material.
The big difference between male and female students in terms of technology can actually be minimized. As it can be seen that male and female prospective teacher can overcome technical problems related to computers. This happens because the use of laptops and LCD have become common place in education. It can be concluded that the difference of knowledge about technology between male and female prospective teacher can be minimized if the technology has been frequently used. The more and more computers are used in schools it will reduce the effect of differences in use between male and female [11].

Conclusion
Based on the result of the research, male and female prospective teacher candidate students are the same for the CK and PK domain. In the TK domain, male prospective teacher knew various types of applications but limited when using it where female prospective teacher knew the various applications but felt difficult when using it. In the PCK domain, male students are more concerned with the suitability of the material that to be taught while the female prospective teacher focus more on the suitability of the applicable curriculum. In the TCK domain, male prospective teacher knew various types of applications but limited when using it where female prospective teacher knew the various applications but felt difficult when using it. In the TPK domain, male prospective teacher choose technology that is effectively and efficiently in terms of learning design while female prospective teacher choose technology that is practical and easy to use. Last in the TPaCK domain, male prospective teacher utilize technology as a reinforcement concept in the learning of the three dimensional material while the female prospective teacher utilize technology only as the presentation of the material in learning three dimension material.

Acknowledgement
I would like to express my gratitude to advisor 1 and 2 who have participated in the writing of this paper until it can be published and useful for many people. I would also like to thank UNESA which has given permission to conduct this research.