The acceptance and use of computers and information technologies has changed the nature of language teaching and learning (Pascarella & Terenzini, 2005). The increased flexibility, convenient access wherever and whenever, the expansion of support, promotion of active learning through in-time learning resources, a teaching pedagogy and learners’ ability to control their own learning are some of the reasons for the popularity of educational programs (Moore, 2005).
The acceptance has not triggered continuous change in how language is learned and taught but has further strengthened the call for autonomous learning. Researchers have posited that it is important for learners to learn independently because it helps in the effective development of learners’ receptive and productive skills (Benson, 2011; Pachler & Field 2001; Schwienhorst, 2007). Initiatives have therefore been made to give students the necessary autonomy. The idea is intended to make students work independently, efficiently gain confidence and get satisfied by developing an interest in the learning process. The intent is also geared at increasing flexibility, independent access, expanding support and promoting active learning by interacting on different platforms (Dabbagh, 2002). Even though studies have shown that online self-study results in successful learning outcomes, others have reported that it does not yield the expected outcome (Weston & Bain, 2010). This is so because in an autonomous online learning environment, learning outcome is determined by factors such as how learners interact, collaborate and construct knowledge. Additionally, expectations from educators and learners themselves may influence how learners independently regulate their study online (Venkatesh, Croteau & Rabah, 2014).
Tell Me More (TMM) is one of the commonly used self-study educational technologies. Tell Me More offers comprehensive support for language learning. This educational language learning technology gives learners the opportunity to learn language anytime and anywhere. It plays a vital role in a self-study environment by adopting the role of a tutor to give meaning, feedback, direct the learning process and evaluating learning. It is used in both English as a second or foreign language contexts to enhance users’ English language skills and knowledge (Levy, 1997; Godwin-Jones, 2010; Blake, 2011).
Interestingly, studies on self-study using computer learning programs have not looked at what learners do when they study independently with the program. Research on Tell Me More have either focused on learners’ perceptions or attitudes and the effectiveness towards learning English through technology, none has focused on what happens or how students use the program for self-study (Yunus, Hasim, Embi & Lubis, 2010; Barrios, 2013; Perez, 2014). Hence, this study did not only focus on investigating what learners did when they used the Tell Me More program for self-study but also how those practices enhanced or undermined the purpose and effectiveness of using the program for self-study.
Investigating what learners’ do while using Tell Me More will not only fill the gap in research on TMM and other educational technologies but also give a holistic insight into what learners do and how it enhances or undermines the purpose of using it for self-study. Probing learners’ self-study practices will enlighten instructors on where learners fall short and practices that need strengthening to ensure effective online language learning for successful learning outcomes. For instructional designers, understanding why, how and what learners do will provide a useful guide for the development of appropriate learning goals and assessment methods. Hence, the research questions focused upon are as follows
The construct self-directed learning has been described as actions directed at acquiring information or skills that involve agency, purpose, goals and instrumental self-perceptions on the part of a student (Pintrich, 2005). Zimmerman (2005), also opined that self-directed learning is the extent to which learners are able to self-regulate themselves to actively participate in learning meta-cognitively, behaviorally and motivationally. Ainley & Patrick (2006) further posited that self-study results from students’ self-regulated thoughts, feelings and behaviors directed towards the acquisition of one’s personal learning objectives.
From the definitions, self-directed learning could be described as both an attitude and the desire for independent learning that is guided or restrained by learning goals and contextual features. What then triggers one’s desires and attitudes are internally generated after countless negotiations with one’s personal psychology and environment after an assessment of the benefits and constraints of the task had been done (Dickinson, 1993). Learners are inspired from their intrinsic cognition and perceive the learning environment with which they as informative rather than an evaluative avenue. Thus, learning goal(s) directs, supports and facilitates learners to self-discover, plan and persuade them to be responsible by encouraging the feeling of personal cause and self-confidence.
Unarguably, one key feature that has advanced online learning is its capacity to stimulate independent learning. Prahtibha (2017) affirmed that the use of computers for language learning does not only make learning ubiquitous but also it encourages self-regulated learning and increases students’ motivation and language proficiency better than classroom study. This notion underlies the introduction and the use of computer assisted language-learning (CALL) technologies and the World Wide Web for language learning. This field of learning popularly conceptualizes as CALL furthers the idea of the learner as an active participant in learning (Green, Alejandro & Brown, 2009) that is “learners learning language in any context with, through, and around computer technologies” (Egbert, 2005, p. 4).
It is evident that learning independently online allows students to demonstrate absolute control of their learning process plan, monitor and evaluate learning progress. This is so because when learners self-direct their study, it allows them to reflect on the learning materials and responses to work at their own pace (Richardson & Swan, 2003).
However, in a self-study online learning environment, learners’ self-directed learning practices and commitment to a task during learning time is difficult to observe and measure. Nonetheless, these could be known through questionnaires and interview by asking students to self-report their learning practices (Appleton, Christenson, Kim & Reschly, 2006; Fredricks, Blumenfeld & Paris, 2004). Self-report involves students reporting on what they did and how they did it when they studied independently.
Therefore, this study aimed at finding learners’ self-study practices including interaction patterns, effort, persistence and any other practices that either enhanced or undermined the purpose of learning online.
Studies have shown that learners who studied independently resorted to specific practices that compensated for the lack of personal interaction that either ensured or undermined learning outcomes (Ulitsky’s, 2000; Murray, 1999; Nielson, 2011). However, there is no existing study on learners’ self-directed learning practices with the stand-alone CALL program Tell Me More.
Researches on TMM have focused on either users’ perception of its ease of use, usefulness, satisfaction and problems encountered. Others have also focused on its effectiveness on improving specific language skill and other languages. For example, a mixed methods study by Gyamfi and Sukseemuang (2018), on TMM users’ satisfaction did not only reveal a high satisfaction with aspects that improved their literary skills (vocabulary, reading and listening) but also the ability to use TMM for self-study, meaningful content and its language learning potential. Stakeholders in the use of educational technologies for language studies were therefore informed to selecting programs with contents that could suit users in terms of their needs and preferences. The study however suggested to appeal to learners’ interest the most and to ensure maximum satisfaction, there should be an improvement in the speaking component of the program to enhance spontaneous interaction.
Additionally, Barrios (2013) investigated the perspectives of 75 university teachers who used Tell Me More for half a year. The findings showed that the teachers were not highly enthused by the program. They were moderately satisfied with Tell Me More as regards how interesting, useful and effective the program was in training them to engage in unplanned conversations and other linguistic uses. However, the findings showed moderate improvement in some communication and linguistic skills such as oral and written comprehension, vocabulary, grammar or pronunciation. Moreover, the learners expressed discontent with some functions of the program, such as the speech recognition that is embedded in the program.
A descriptive study on undergraduate students’ achievement with TMM at four proficiency levels revealed an improvement in the proficiency level of beginners and advanced users (Gyamfi & Sukseemuang, 2017). Interestingly, TMM had no to little impact on the English proficiency of students at the intermediate+ and intermediate levels. A further statistical and qualitative analysis revealed that the significant differences between users of different proficiency groups may have accounted for the surprising findings. Specifically, the difference in time of use, learning goals and mode assessment accounted for the differences in outcomes.
Furthermore, Perez’s (2014) study on users of Tell Me More in a Philippine university showed that in terms of effectiveness in enhancing their communication skills, there was no significant difference between the medical and para-medical students. Users further disagreed that they encountered difficulties while using the language resource.
These studies have shown that Tell Me More as a self-instruction tool has strengths and weaknesses and supports the notion that learners may devise their own ways of using the program. However, researchers have focused on other dimensions of research to the detriment of investigating what happens when learners use the program for self-study. It is therefore necessary to investigate what happens in order to facilitate learning interventions for effective autonomous online learning.
As part of their course requirements, EFL undergraduate students from different faculties in a university in the south of Thailand enrolled to use the Tell Me More computer-learning program. The participants from 2015 academic year could use the program at a students’ self-access learning center and any place and time of their convenience. The users took a placement test incorporated in the TMM program before having access to the main content in the program. The test aimed to determine their level of proficiency to be assigned specific contact hours to use the program. There were four proficiency levels. For their learning goals, the beginners had to use the program for 50 hours, 40 hours for the intermediate level, 30 hours and 20 hours for the intermediate+ level and advanced levels respectively. They took a progress and an achievement test in the middle and at the end of the term respectively to measure their progress and overall achievement. The practices of learners were surveyed at the end of the academic year 2015 using the Effort and Persistence in Learning (EPL) subscale of Students Approaches to Learning Survey (Artelt, Baumert, Julius-McElvany & Peschar, 2003).
A mixed methodological approach was used in this study. A questionnaire and semi structured focused group interview were used as the data collection instruments. Based on Krejcie and Morgan (1970), 350 out of 2,137 university EFL students were randomly selected for the study. Among the 350 students surveyed, 26% (91) students were males while 74% (259) were females. They were from different faculties and proficiency levels. They successfully completed using the TMM program for the required number of study hours in the 2015 academic year. Ten (10) participants who were from different proficiency levels and faculties were randomly and conveniently involved in a semi-structured focused group interview.
A four point Likert scale questionnaire ranging from 1= “Almost never” 2= “Sometimes” 3= “Often” 4= “Almost always” was used. The items in the questionnaire were based on Effort and Persistence in Learning (EPL) subscale of Students Approaches to Learning Survey (Artelt et al., 2003). Artelt and her colleagues developed the EPL to examine how learners approached learning based on their motivation, self-related beliefs and learning strategies. It originally consisted of a 4-item scale. However, the EPL was modified to ask students about what they did and how they used the Tell Me More program in a more beneficial way. This comprised of their effort, preferences and co-operation with the guidelines for using the program. This was done to make the items have a direct relation with the regulation of their own learning. The researcher ended up with 8 items, which were appropriate for the research context and purpose. The questionnaire was originally written in English and was translated into Thai by a bilingual expert who is socio-linguistically competent and already had knowledge about the research. Three experts in educational technology reviewed the content validity and compatibility of both the English and Thai versions of the questionnaire. The Thai version was piloted among 50 students who used the TMM program in the summer of the academic year 2015. The items recorded a Cronbach alpha coefficient value of ¥á = .63 which is an adequate value for internal reliability of a scale (DeVellis, 2003).
As regards data collection, the questionnaires were distributed at the end of their use of the program. In order to get a high response rate, two techniques were used for data collection. Firstly, the researcher resorted to distributing the questionnaires to students in class after an announcement had been made. The distribution and collection of questionnaires was done at different class hours. The second method of distribution was through snowball technique (Heckathorn, 1997). Here, the researcher found some subjects for the study from the selected faculties and gave them copies of the questionnaire for onward distribution to students who used the program in the 2015 academic year. Because of the large population, 450 questionnaires distributed, there was a high return rate of 350 questionnaires. However, 10 of them were either incomplete or badly filled and were excluded from the analysis. The entire data collection process took two weeks.
A semi structured focused group interview based on student approaches to learning and their efforts and persistence (Artelt et al., 2003). An interview collected data to confirm the findings of the questionnaire. It was structured to have an in-depth examination into specific practices and approaches of learners with the TMM program. Since the questionnaires elicited data without any explanations, this instrument augmented the findings by providing a richer and a more precise date for inferences to be made.
The participants for the focused group interview were invited by phone call and an in-class announcement. For the phone calls, twenty (20) participants were randomly selected based on the responses in the questionnaire and a class announcement was made at the faculties that took part in the survey. Ten (10) students showed up for the interview at the scheduled time. A bilingual expert in both Thai and English who already had knowledge about the research conducted the focused group interview. He was briefed on how to manage the interview process by creating rapport and keeping the respondents focused. The participants were assured of the confidentiality of their response to assure them that their response will not be revealed under any circumstances. The interview was videotaped and it lasted between 30 to 45 minutes.
The data from the questionnaire was statistically analyzed to find the frequency, percentages, means and standard deviations using an SPSS program. The means scores were interpreted according to Phongwichai (2008) as follows 1.00-1.75 (Very low), 1.76-2.51 (Low), 2.52-3.27 (High) and 3.28-4.00 (Very High).
For the focused group interview, the responses were transcribed and translated into English by the bilingual expert who conducted the interview. For the transcription, the translator listened to and transcribed the responses twice from the recorded video tape. The second transcription was done to ensure its consistency with the first transcript. Both transcripts were compared to ensure its reliability and credibility. It was then translated into English and subjected to content analysis. The analyses were later categorized into themes to complement the results from the survey.
The findings from the questionnaire and interview were concurrently analyzed and further subjected to thematic categorization. This technique complemented the limited amount of information that was elicited from the questionnaire for richer and more precise inferences.
As shown in Figure 1 the first item asked learners to rate their instruction reading practices. The results indicated that 39% and 37% representing 265 of the students “sometimes” and “often” read the instructions for every activity they did. 20% (68) students reported that they “almost always” read the instructions while 4% (14) students said they “almost never” read the instructions before they do the activities. The high mean score (X = 2.73) recorded for this item is an indication of learners’ positive instructions reading attitude (Table 1).
|1.||I read the directions for every activity before I start to practice.||2.73||.825|
|2.||I keep trying an activity until I get the correct answer.||2.72||.803|
|3.||I skip to new activities when I face difficulties.||2.65||.885|
|4.||I look at the answers in the answer key when I answer a question incorrectly.||2.54||.863|
|5.||I go to the answer key immediately to do the activities.||1.87||.812|
|6.||I leave the program on to count the time.||2.45||.879|
|7.||I ask someone to do the activities for me.||1.47||.777|
|8.||I find help from other materials (google translate, dictionary, google).||2.24||.863|
The second had the second highest mean score of (X = 2.72). The high mean score does not only show the efforts learners’ made but also their persistence during their self-study with Tell Me More. The findings also showed that majority of the students, 39% and 38% representing 265 students reported that they “sometimes” and “often” kept trying an activity until they got the correct answers. 19% (63) of the students reported to have “almost always” tried until they got the right answer while 4% (12) students indicated that they showed no effort.
With regard to what learners did when they faced a challenge in answering a question, the findings indicated that 40% (135) students sometimes skipped an activity whenever they found it difficult while 33% (111) students often skipped an activity when they cannot do it. 20% (68) “almost always” skipped an activity while 8% (26) “almost never” skipped an activity. The high mean score (X = 2.65) of this item suggest an inconsistency in learners’ report of trying until they got the right answer to a task (item 1).
The questionnaire further asked participants to rate what they did when they got an answer wrong. The findings indicated that 31% (106) and 16% (50) of students often and almost always consulted the answer immediately they got an answer in an activity wrong. 45% (152) sometimes did that while 8% (27) almost never immediately looked at the correct answer whenever they got a question wrong. The mean score for this item was at a high level of X = 2.54. This also shows the inconsistencies in what the learners report of trying until they got the correct answers (Item 1).
As shown in Figure 2, it was found out that 36% (122) “almost never” went straight to the answer key to do the activities. 45% (153) of the respondents also indicated that they sometimes went to the answer key first before doing the activities. 15% (51) often went to the answer keys for solutions while 4% (14) “almost always” look at the answers before doing the task. The mean for this item was X = 1.87. The low mean score for this item suggest that the learners made some efforts in doing the tasks in the program. It therefore points to the efforts learners made while they used the program and to some extent explain why students reported that they kept trying until they got a question right (item 2).
As regards the time spent on the program, it was revealed that 13% (42) almost never left the program on to count the time while 44% (151) sometimes left the program on to count the time, 29.4%(100) often left it on to count the time. 14% (47) reported to have “almost always” left the program on to count the time. This item had a high mean of X = 2.45. The low mean score for this item suggest that the learners may not have left the program on to count the time.
The findings further indicated that 65% (222) almost never made others do the activities or task in the program for them. While 22% (74) showed that students sometimes made others do the work in the program for them but it was minimal, there was a 10% (33) indication that the students often did that. 2% (8) reported to have always made others do the work for them. This item recorded a very low mean score of X = 1.47. Though the mean score of this item was very low, the percentages mean that majority of the students showed a great sense of responsibility by doing the task in the program on their own.
The results further revealed that 48% (163) and 27% (91) sometimes and often found help from other materials while using the program. 7% (23) almost always resorted to other materials for help while 18% (60) never sought help from other materials such a google translate or dictionary or the grammar book. The mean score for this item was low at x= 2.24. Although this item has a low mean score, high percentage of learners sometimes and often resorted to other forms of materials in addition to the content of the TMM program to enhance their self-study. This item further confirms learners’ actions of trying until they got the answer correct (item 2). Learners may have kept trying by resorting to other materials.
Since self-study does not imply learning in isolation, the learners reported to have multitasked by sometimes and often consulting other sources such as google translator, online dictionaries and other supplementary materials for better understanding (item 8, figure 2). The learners may have also multitasked because they may have found other sources of information as relevant to their unconscious acquisition of language. This shows the freedom of choice or flexibility the online learning program gave the learners. The internet provided learners many ways and options of making self-study through different media possible, easy and effective. Hence, the TMM program eased and enabled learning practices beyond its immediate online learning environment. This finding confirmed Jarvis’ (2013) study that EFL learners make use of other computer-based resources to aid their conscious learning of English language. This is evident in one participant’s response. He says:
“The way the program is set up encourages me to seek help from other sources. Sometimes there are no explanations further to where and why I got an answer wrong. This raises motivation to search further for help to know where I am completely wrong.”
Additionally, the learners’ sometimes and often left the program on to count the time (item 6, figure 2). One reason that may have accounted for this practice as revealed in the focused group interview was that assessment of the course for which the Tell Me More program was a part of was based on the number of hours spent on the program. Hence, students may have focused on fulfilling the time requirement as opposed to learning the content in the program. Learners saw leaving the program on to count the time as an easy approach to gain scores and fulfill the program’s requirement. Moreover, what may also hold true is that the learners may have finished doing the assignments in the program before the required time. Hence, they left the program on to fulfill the time requirement. This finding not line with Matuga (2009) study that learners would attend an online learning course may exploit other means for good grades. It also supports Bacow, Bowen, Guthrie, Lack and Long (2012) and DeGagne and Walters’ (2010) study that giving time restrictions to students may not motivate learners to learn. Some respondents gave the following comments during the focused group interview
“I leave the program on to count the time because most of the time I finish doing the activities in the program before the required number of hours. Therefore, the only way to get the grade is to leave the program on since the hour is still needed.”
Another respondent remarked:
“I do not focus on the hours of use. I focus on the content but if I continue to do that, I will end up not fulfilling the minimum hours. Therefore, I leave it on however I think there is no need to focus on the hours but the questions that are answered correctly.”
Comments from two respondents confirmed speculations that the students left the program on to count the time without doing the activities. They said:
“Since it is only the time that is needed to get the score for using the program, I leave it open for the time to count so that I will get the score at the end of the semester. It is an easy way out.”
“I leave the hours to count because it is only the hours that is needed for the grades. When I do this I can study other subject and will not waste my time on the program”
The findings on the time further signify that learning goals had the capacity to influence students’ practices. Therefore, to demonstrate a workable time management strategy to avoid leaving the program on to count the time, assessment of learning progress in autonomous online learning should not be solely based on time (Roper, 2007). There should be innovative ways to assess learning progress that also focuses on content.
The learners’ showed responsibility by first reading the instructions of the learning activities before they started using the program (item 1, figure 1). They also showed eagerness and motivation to learn by constantly trying an activity until they got the answers correct (item 2, figure 1). The learners also did not ask other people to do the activities for them (item 7, figure 2). This shows their readiness, acceptance and the sense of responsibility for autonomous learning. One respondent during the interview affirmed this
“I cannot rely on anybody to do the activities in the program for me because everybody is using the program and responsible for the outcome at the end of the semester. I had to put in effort to answer the questions in the program correctly to make me feel good.”
However, some learners often and almost always skipped when they faced tasks that were challenging or beyond their ability (item 3, figure 1). In addition, their practices of sometimes and often looking at the answers before doing the activities and immediately after trying once obviously undermined the efficacy of the program (figure 2). One participant said during the interview:
“I do not know how to find help from other internet sources; I just skip when the activity it is higher than my level of ability or when I cannot use the activity in my daily life. Moreover, I look at the answers in the answer key.”
These unstable learning practices signify that students may not be able to control themselves in their self-study with programs that contain in-built answers. These behaviors may not help instructors know the real impact of the program on students English language ability. These findings support Waemusa, Srichai and Wongphasukchote (2008) study that learners may demonstrate unstable learning practices in their online self-study learning process. However, this aspect of self-study is difficult to control because of the lack of external monitoring. It further confirms Sukseemuang (2009) findings and recommendation that though learners may favor self-directed learning, they may however need some form of control to engage in the right learning practices.
Through the self-report, it was noted that majority of the students exhibited considerable effort in their instruction reading practices (item 1, figure 1). This showed that the users read and understood the instructions before they started doing the task in the program. They also showed commendable efforts and persistence to get the correct answer whenever they answered a task wrongly (item 2, figure 1). This showed students desire to use the program to improve their level of English. Moreover, they almost never asked others to do the activities for them (item 3, figure 2). When asked about the efforts they made while they used the program, one respondent said,
“When I use the program, I have to think hard before I can complete the activities. Though it makes it less fun, it helps me improve my English. I can see about 70% improvement in my English language skills and it is because I keep trying.”
These could be seen as behaviors that do not only show learners interest in a task but also signify that learners are benefitting from the task. However, students’ self-report of skipping to new activities whenever they faced some challenges and looking at the answers before doing the task may counteract their effort. Hence, learners are likely to skip other activities when they find the current activity they are on difficult or irrelevant for their level. On the other hand, they may persist if the find the activities they are engaged in meaningful or relevant. Skipping to new activities when they face challenges suggests that learners may need support or assistance in their self-study. It also denotes that learners may not be interested in using the program or the program was not relevant enough to sustain their interest for a long time or even lack self-control in their autonomous study. It further confirms the study by Shea, Pickett and Li (2005) and Mason et al. (2010), that learners may feel unmotivated to learn online for many reasons such as relevance or difficulty of a task. Learners’ practices of looking at the answers before doing the task may undermine the effectiveness of the program (item 1, figure 2). It may not help instructors know the actual effect of the program on students’ performance. This is evident in a comment made by one of the respondents during the interview:
“I have no time to find help from other internet sources; I skip to a new activity when I find the current on challenging for me. I sometimes also go to the answer key for solutions”
Even though students showed effort and persisted to benefit from the program to improve their level of English, they still found ways to cheat by looking at the answer key before doing the activities in the program. They sometimes and often left the program to count the time without learning the content in the program to show learning progress. These practices undermined the effectiveness of the program. This finding confirms the studies by Bacow et al. (2012) and DeGagne and Walters (2010) that specific time commitment may push students to use a learning technology. It is however not in line with the study by Orr, Williams and Pennington (2009). However, the principles of self-directed learning such as learner involvement, information searching skills, freedom of choice and selection or skipping of task that were challenging, meaningful or relevant or otherwise clearly guided their independent study (Little, 2006).
In sum, the study revealed that learners self-direct their online learning in ways that included multitasking, leaving the program to count the time and other inconsistent self-study practices. Learners multitasked not only to complement the limited information in the program but also to show their persistence and interest in a task. This enhances learning outcome. However, learners left the program on to count the time to satisfy the time requirement for assessment purposes. This mean that whereas some students will genuinely use the program to improve their proficiency, others with strong technological skills will manipulate the program to their advantage by exploiting technological loopholes in order to satisfy the requirement of the program (Haber & Mills, 2008). Lastly, for learners to engage in effective and consistent demonstrate without any external monitoring, curriculum designers could design appropriate learning goals and assessment methods to ensure consistency and measure learning progress.
The findings imply that learning goal influences self-study practices, it is therefore recommended that time should not be the sole measure learning progress. An additional means such as getting a specific score in an achievement test to learners will help learners know the effect of the program on their English language skills. Moreover, Quantifiable ways such as incorporating contents of online learning programs in written tests could be used to supplement assessment to measure learning progress.
Due to the multitasking and inconsistent practices of learners, it is suggested that learners could be trained on how to search for additional information and what to do when they face a challenging task. They could be trained on some appropriate self-study skills such as seeking assistance from appropriate online sources to help smoothen the learning process. This may help minimize the practice of seeking answers before they do the activities and after trying once. It could also help learners monitor and evaluate themselves in their learning process, have a clear sense of direction on how to set goals, select strategies and control their learning process to become successful online learners.
There were some limitations in this study in terms of the methodology. The study utilized only self-report survey responses and interviews of learners. An additional research method such as observation would have made the findings more comprehensive. The study also focused on learners’ practices without considering the perceptions that influence these practices and how these practices correlates with students’ performance. It is suggested that further studies could focus on how learners’ perceptions influence their practices and how these practice affect learners’ performance.
This research was supported by the Higher Education Research Promotion and the Thailand’sEducation Hub for Southern Region of ASEAN Countries Project Office of the Higher Education Commission.
Ainley, M., & Patrick, L. (2006). Measuring self-regulated learning processes through tracking patterns of student interaction with achievement activities. Educational Psychology Review, 18(3), 267–286. http://dx.doi.org/10.1007/s10648-006-9018-z
Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44(5), 427–445. https://doi.org/10.1016/j.jsp.2006.04.002
Bacow, L. S., Bowen, W. G., Guthrie, K. M., Lack, K. A., & Long, M. P. (2012, May 1). Barriers to Adoption of Online Learning Systems in U.S. Higher Education Ithaka S+R. https://doi.org/10.18665/sr.22432
Barrios Espinosa, E. (2013). Learning English using Tell Me More: Perspectives of university teaching staff as users of the online application. Revista de Educación, 362, 506–534. https://doi.org/10.4438/1988-592X-RE-2013-362-240
Blake, R. (2011). Current Trends in Online Language Learning. Annual Review of Applied Linguistics, 31, 19–35. https://doi.org/10.1017/S026719051100002X
Dabbagh, N. (2002, April 29). Using a web-based course management tool to support face-to-face instruction. The Technology Source. Retrieved from http://media.sabda.org/alkitab-1/Pdfs/Dabbagh-UsingaWeb-BasedCourseManagementTooltoSupportFacetoFa.pdf
DeGagne, J., & Walters, K. (2010). The lived experience of online educators: Hermeneutic phenomenology. Journal of Online Learning and Teaching, 6(2). Retrieved from http://jolt.merlot.org/vol6no2/degagne_0610.html
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. https://doi.org/10.3102%2F00346543074001059
Godwin-Jones, R. (2010). Emerging technologies tools and trends in self-paced language instruction. Language Learning & Technology, 11(2), 10–17. Retrieved from http://llt.msu.edu/vol11num2/emerging/default.html
Green, T., Alejandro, J., & Brown, A. (2009). The retention of experienced faculty in online distance education programs: Understanding factors that impact their involvement. The International Review of Research in Open and Distance Learning, 10(3), 1–15. https://doi.org/10.19173/irrodl.v10i3.683
Gyamfi, G. & Sukseemuang, P. (2017). Effect of “Tell Me More” on EFL Undergraduate Students’ English Language Achievement Open Praxis, 9(4), 1–13 https://doi.org/10.5944/openpraxis.9.4.724
Gyamfi, G. & Sukseemuang, P. (2018). EFL Learners’ Satisfaction with the Online Learning Program, Tell Me More. Turkish Online Journal of Distance Education, 19(1), 183–202. https://doi.org/10.17718/tojde.382798
Haber, J., & Mills, M. (2008). Perceptions of barriers concerning effective online teaching and policies: Florida community college faculty. Community College Journal of Research and Practice, 32, 266–283. https://doi.org/10.1080/10668920701884505
Heckathorn, D. (1997). Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations. Social Problems, 44(2), 174–199. Retrieved from http://www.respondentdrivensampling.org/reports/RDS1.pdf
Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
Mason, J., Hickman, C., Dyer, A., Koproske, C., Fry, G., & Taha, M. (2010). Engaging faculty in online education: Rightsizing incentives and optimizing support. Washington, DC: University Leadership Council of The Advisory Board Company. Retrieved from http://www20.csueastbay.edu/oaa/files/student_success/EngFacOnlineEd.pdf
Moore, J. (2005). Is higher education ready for transformative learning? A question explored in the study of sustainability. Journal of Transformative Education, 3, 76–91. https://doi.org/10.1177%2F1541344604270862
Nielson, K. (2011). Self-Study with Language Learning Software in The Workplace: What Happens? Language Learning & Technology, 15(3), 110–129. http://dx.doi.org/10125/44265
Orr, R., Williams, M., & Pennington, K. (2009). Institutional efforts to support faculty in online teaching. Innovative Higher Education, 34(4), 257–268. https://doi.org/10.1007/s10755-009-9111-6
Pintrich, P. R. (2005). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, and M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego, CA: Academic.
Pratibha, M. (2017). Promoting Self-Learning in Developing Communication Skills of Technical Students. IRA International Journal of Education and Multidisciplinary Studies, 6(1), 1–8. http://dx.doi.org/10.21013/jems.v6.n1.p1
Shea, P., Pickett, A., & Li, C. (2005). Increasing access to higher education: A study of the diffusion of online teaching among 913 college faculty. The International Review of Research in Open and Distance Learning, 6(2). https://doi.org/10.19173/irrodl.v6i2.238
Ulitsky, H. (2000). Language learner strategies with technology. Journal of Educational Computing Research, 22(3), 285–322. https://doi.org/10.2190%2FVLTU-CCXF-NFYL-277H
Venkatesh, V., Croteau, A. M., & Rabah, J. (2014). Perceptions of effectiveness of instructional uses of technology in higher education in an era of Web 2.0. Paper presented at the 47th Hawaii International Conference on System Sciences, Waikoloa, HI.
Waemusa, Z.; Srichai, C. & Wongphasukchote, K. (2008). A study of Thai learners’ responsibility in learning a foreign language. Prince of Songkla University. Department of Languages and Linguistics. Retrieved from http://kb.psu.ac.th/psukb/handle/2010/8003
Weston, M.E., & Bain, A. (2010). The end of Techno-critique: The naked truth about 1:1 laptop initiatives and educational change. Journal of Technology, Learning and Assessment, 9(6). Retrieved from https://ejournals.bc.edu/ojs/index.php/jtla/article/view/1611
Yunus, M., Hasim, H., Embi, M. A. & Lubis, M. A. (2010). The Utilization of ICT in the Teaching and Learning of English: ‘Tell Me More’. Procedia. Social and Behavioural Sciences, 9, 685–691. https://doi.org/10.1016/j.sbspro.2010.12.218
Zimmerman, B. (2005). Attainment of self-regulation: A social cognitive perspective. In M. Boekaerts, P.R. Pintrich, & M. Zeidner (Eds.), Handbook of self –regulation (pp. 13–39). San Diego, CA: Academic Press.