How, and to what extent, are digital tools used to support self-directed learning (SDL) at the Western Academy of Bejing (WAB)?

October 11, 2017

MBrookesCapstoneResearchProject2017

Introduction

Digital technologies in conjunction with appropriate strategies afford greater agency to learners through increasing choice over what, when and where they learn, how long they learn for and the path they take in their learning (D. Song & Bonk, 2016; Y.-F. Yang, 2015). Learning experiences are becoming social and participatory and include rich digital media (Du Toit-Brits & Van Zyl, 2017). Until recently self-directed learning (SDL) was associated with higher education and the adult learner; however, due to the availability of mobile technologies and access to online learning and open resources, opportunities for students to self-direct their learning in school contexts are emerging (Bartholomew, 2016; Du Toit-Brits & Van Zyl, 2017; Fahnoe & Mishra, 2013; Rashid & Asghar, 2016; Robertson, 2011; Saks & Leijen, 2014; L. Song & Hill, 2007).

Purpose

The purpose of this study is to investigate the digital behavior of Capstone students at the Western Academy of Beijing (WAB) to understand how, and to what extent, digital tools can support the SDL experience for high school students. This study will ascertain:

1. a deeper understanding of the attitudes required of a self-directed learner and the process of SDL;
2. an understanding of how digital tools, in conjunction with appropriate learning strategies, might support SDL experiences;
3. insight into the challenges and barriers faced by students in using digital tools to support SDL;
4. recommendations to promote the use of digital tools and strategies to support SDL.

 

Context

The Western Academy of Beijing (WAB) is an international school that offers a number of SDL initiatives, including the Capstone program, a two-year self-directed course for students in Grade 11 and 12 (16-18 year olds) who have a particular passion or area of interest that sits outside or goes beyond the high school curriculum, the International Baccalaureate Diploma Program (IBDP). The Capstone program is based on a model of self-determined learning (Hase & Kenyon, 2000) where students are guided through a mentoring system to co-construct a personalized curriculum for 20% of their learning time. Capstone Students set their own learning goals, plan and implement their learning paths and devise indicators to assess their learning. Credit is awarded at the end of each semester, through their Defense, where each student presents and defends their learning journey and achievements to an academic panel. WAB provides a mature, technology-rich learning environment, including a 1:1 program with access to a wide range of digital tools, systems and online resources. Therefore, it is assumed that Capstone students are well-equipped to use their mobile devices and digital technologies to facilitate and augment their learning to effectively pursue their learning goals. This study explores and challenges this assumption.

Theoretical Framework

SDL has many interpretations (e.g. Candy, 1991; Grow, 1991; Guglielmino, 1977; Hiemstra & Brockett, 2012; Oddi, 1987) although most definitions stem from the work of Knowles (1975), who defines SDL as a process,

in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes (1975, p. 18).

This process is illustrated in Figure 1.

 

 

Figure 1. Model of the learner-driven process (Knowles, 1975)

Beyond the learner driven process, SDL considers the internal cognitive dispositions of the learner. This includes the learner’s capacity to accept responsibility for meaning making, and to monitor their own learning (Brookfied, 1986, Du Toit-Brits & Van Zyl, 2017; Guglielmino, 1977; Oddi, 1987).

Garrison (1997) proposed a model that integrates these internal cognitive dispositions, in three overlapping dimensions:

1. self-management which encompasses the management of the context, including the social setting, resources, and actions (L. Song & Hill, 2007);
2. self-monitoring where learners monitor, evaluate and regulate their learning strategies (L. Song & Hill, 2007);
3. and, motivation which drives the decision of the student to “focus on and persist in learning activities and goals” (Abd-El-Fattah, 2010, p. 587).

These are shown in Figure 2.

 

Figure 2. Garrison’s dimensions of the self-directed learner (1997)

WAB defines SDL as, “teaching students how to learn for themselves, where learners take responsibility to set goals, access resources and choose strategies for learning (2016, para. 2)”. As WAB’s definition outlines the broad steps in the SDL process and indicates some of the dispositions required to become a self-directed learner, the theoretical framework for this study was based on the SDL model from Knowles (1975) and the Garrison’s dimensions of SDL (1997).

Research Design

This study used a qualitative approach to explore how, and to what extent, digital tools were used to support SDL (Yin, 2003 in Baxter & Jack, 2008). This study evolved over a two-week period, six weeks into the first semester of academic year (2017-18) and used multiple data sources, including a survey, interviews, direct observations and extensive document analysis. The study was guided by the theoretical framework outlined above to make the research findings meaningful and generalizable by linking together findings into a coherent structure (Green, 2014).

Participants

Initially, all six participants had been selected from the Capstone program; however, as the two Grade 11 students were at the very early stage of the program and had minimal experience of directing their own learning, this data was discounted. The participants selected were the four students in the second year of the Capstone Program, each of whom had been successful in the first year of the program. Each student had a unique learning focus (photojournalism, choreography, digital art and computer science), and employed their own set of digital tools and strategies to support their learning. Table 1 below summarizes their characteristics.

Student Id	Learning Focus	Gender	Grade	Key hardware  used	Examples of Learning
PJ-12	Photojournalism	M	12	MacBook, Smart Phone, Camera	Preparing exhibition of work from G11 (curation skills) Developing editing skills and organization of images
D-12	Dancer: Choreography & teaching	M	12	MacBook, Smart Phone	Developing choreography for performance(s) Teaching G12 & G3 students to dance
DA-12	Digital Artist	F	12	MacBook Bamboo touchpad with stylus	Creating portfolio for art college Design of logo for British Club of Beijing
CS-12	Computer Scientist	M	12	MacBook, PC, Smart Phone	Development of weather app Learning to program with Java

Table 1: Characteristics of Capstone Students

Data Collection

The methods used to collect data included a survey, interviews and a document analysis. A comprehensive literature search was conducted to select a suitable survey instrument for self-directed readiness. The surveys considered are summarized in Table 2 below.

Survey Instrument	Creator	Focus	Suitability
Self-directed learning implementation skills scale for primary school students	Feryal & Selv (2016)	Primary School students	Unsuitable as too simplistic as lacked skills for planning and goal setting.
The self-directed learning with technology scale (SDLTS) for young students	Teo et al (2010)	Primary School students	Unsuitable as too simplistic as comprised five questions.
Self-Directed Learning Readiness Scale	Guglielmino (1977)	Adult learners	Unsuitable as only available under license.
Self-Rating Scale of Self-Directed Learning (SRSSDL)	Cadorin, Cheng, & Palese (2016)	Adult learners	Unsuitable as based only on Knowles process model (1975).
Self-Directed Learning Readiness Scale for Nursing Education (SDLRSNE)	Fisher & King (2010)	Adult learners in the field of nursing	Unsuitable as adapted for specific target audience (nurses)
the Self-Directed Learning Aptitude Scale (SDLAS)	Abd-El-Fattah (2010)	High school students	Suitable as based on work of Knowles (1975), Brookfield (1985), and Garrison (1991, 1997).

Table 2: Summary of survey instruments for self-directed readiness

The Self-Directed Learning Aptitude Scale (SDLAS) (Abd-El-Fattah, 2010) developed for high school students was used to measure students’ aptitude to SDL based on a review of the research of Knowles (1975), Brookfield (1985), and Garrison (1991, 1997). Students self-reported on statements in the three dimensions, self-management, self-monitoring and motivation, using a 5-point Likert scale, ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5) (see Appendix A).

Face-to-face semi-structured interviews, between 30 and 45 minutes in length were conducted with students which were recorded then transcribed and coded using NVivo software. The students were asked to explain how they used technology in each of the following categories: organization, collaboration and communication, selection of resources, learning, and assessing learning; additionally, they were asked to comment on challenges and recommendations.

The document analysis comprised students’ reflective blogs, student artifacts and Capstone program documentation, such as field notes and Defense reports (see Appendix B).

Table 3 below summarizes the type of data collected and the metrics.

Data	Metrics
The Self-Directed Learning Aptitude Scale (SDLAS) survey (Abd-El-Fattah, 2010)	5-point Likert Scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5).   Statements organized into 3 dimensions Self-management – 8 factors ·       Self-monitoring – 8 factors ·       Motivation – 9 factors   Average score for each dimension calculated for overall score between 1 and 5
Interviews: 1.      D-12(20 Sept 2017), 2.      PJ-12 (25 Sept 2017) 3.      CS-12 (25 Sept 2017) 4.      DA (29 Sept 2017)	Qualitative data: audio recorded through GarageBand (mp3 format); transcribed & coded using NVivo software.
Document Analysis: ·       Review of selected student blog posts from August 2016 to Sept 2017 ·       Reports from Defense Meetings (January 2017 and June 2017) ·       Observation and field notes of ToR for PJ, CS, D and DA  (August 2016 – Sept 2017).	Documents scanned and partially coded to provide supporting evidence to findings from survey and interviews.

Table 3: Data Collection and Metrics

Data Analysis

The analysis, represented in Figure 3, focused on the relationship between the students’ selection and use of digital tools and strategies in each of the steps in the SDL process (Knowles, 1997) and dispositions of self-directed learner based on Garrison’s (1997) model.

 

 

Figure 3 Relationship between SDL process, dispositions & digital tools

 

This is summarized in Table 4 below

Knowles (1975)	Garrison (1997) Dimension			Digital tools & strategies investigated
	Self-management examples	Self-Monitoring examples	Motivation examples
Step 1: determining the learning needs	Management of the context, including the social setting, resources, and actions		May not be evident by the use of a particular tool but may become evident in the strategy used by student with a particular tool or set of tools.   For example: critically evaluate new ideas and knowledge	Tools for organization: planning & file storage
Step 2: expressing the learning aims clearly and implicitly		Making connections		Tools for collaboration and communication
Step 3: determining the learning materials	Selection and management of resources	Monitors, evaluate sand regulates learning strategies		Tools for research and selection
Step 4: selecting and implementing appropriate learning strategy Going beyond self	Plans solutions	Monitors, evaluate sand regulates learning strategies		Tools for learning
Step 5: assessing learning outcomes.	Updating reflection blog	Awareness of own weaknesses; sets up criteria to evaluate performance; judges own abilities fairly; self-correction.		Tools for assessment

Table 4: Summary of relationship between SDL process, dispositions & digital tools

Findings & discussion

The results of the self-reported SDLA survey shown in Figure 3 below show low scores for self-management and high scores for self-monitoring and motivation.

 

Figure 4 Capstone Students SDLA Scale Results

To a large extent these scores were supported by the findings from the interviews and subsequent document analysis.

Use of Digital Tools

The research showed that each student employed digital tools and strategies for most of the steps of the learning process as defined by Knowles (1975) as shown Table 5 below:

 

Type of Digital Tool	PJ-12	D-12	DA-12	CS-12
Organization	O365 Calendar	Evernote	MS Word	Sticky notes (MacOS)
Collaboration &communication	O365 Email WeChat	O365 Email WeChat	O365 Email	O365 Email WeChat Skype
Research & selection	YouTube Online Documentaries	YouTube	Search engines	Java tutorials
Learning	Online tutorial videos Lightbox Photoshop	Video Camera for recording Video playback for analysis	Coursera online course Photoshop Adobe Illustrator	Online forums Program development tools such as IDEs for Java, XCode
Assessment of Learning		Video Analysis		Online quizzes

Table 5: Digitial tools and strategies employed by students

 

Self-management and digital tools

By assuming responsibility for their learning, a self-directed learner needs to take charge of processes including planning their own learning, setting deadlines and selecting and organizing resources (Fahnoe & Mishra, 2013). These processes can be supported through digital tools and are considered fundamental concepts of technology operation as outlined in the International Society for Technical Education (ISTE) Standards for Students (2017). As evident through the survey results for self-management, the findings showed that the student skills in these areas were limited. The key findings are discussed below.

Planning: Strategies for planning and recording deadlines were mostly simplistic. Planning tended to be short term – students generally mapped out current projects or work flows as ‘to-do’ lists, documented using tools that included Evernote, which synchronizes notes across devices, and electronic calendar reminders. CS-12 preferred the constant appearance of the sticky notes on the desktop as this “constantly reinforces that I have to do this”. While most used their electronic calendars with automatic calendar notifications, CS-12 felt he was unable to pinpoint actual dates and set weekly timeframes instead. Long term strategic planning was vague even though students were required to plan out each semester and post on their blog. Students tended not to revisit or revise their plans once committed in a post resulting in a disconnect between what they actually did and what they had planned at the onset of each semester. Only one student, D-12, reviewed his planning process by setting regular scheduled meetings with his mentor to ensure that tasks were completed and within the deadlines.

Digital File Management: Analysis and evaluation, both higher-order cognitive processes, are required in determining what, where and how to manage files by considering the storage, organization and retrieval processes of both digital and non-digital files and resources (Anderson et al., 2001). Students preferred to use digital versions of files and again selected different storage options comprising storage on mobile devices or in applications such as Evernote which synchronizes data to connected devices. In one case, the data was stored and organized by the mentor (D-12). Even physical artefacts such as videos of dance practices, and planning of activities sketched out on paper, in notebooks or physical whiteboards, were captured electronically by students on their smartphone. However, despite students judiciously digitizing and storing digital copies of their learning materials and resources, a consistent observation was all students’ inability to organize their files systematically and consistently, resulting in slow retrieval of files, duplication of files and loss of files due to failure to back-up. These all resulted in inefficient time management practices and a decline in their readiness for learning.

Selection of resources: Lin (2008) reported that the use of online resources helps students diagnose their own learning needs, make choices about their own learning paths and conduct independent inquiry to deepen their knowledge. The study showed that the strategies for searching and selecting learning materials from the internet tended to commence with basic Google searches. Many students used YouTube for how-to videos when learning a new software skill and most reported viewing a number before selecting one or more for instruction (e.g. PJ-12, DA-12). Additionally, two students sought out experts in their fields through online forums in search of solutions (CS-12, DA-12). Students did not use any library databases nor did they consult with the librarian for help with developing strategies. Further development of digital search strategies is certainly required to support these self-directed learners seek out and connect with experts in their field and access richer learning resources.

Self-monitoring and digital tools

Self-monitoring can be predicted by self-management (Abd-El-Fattah, 2010). Effective self-management requires highly developed cognitive skills, whereas self-monitoring requires a metacognitive approach where students continually assess their performance. This metacognition cycle is shown in the diagram below.

Figure 5 Cycle of Metacognition (Garrison, 1997)

The evidence presented in the students’ reflective blogs showed that students’ formal reflection of their learning was limited despite the extensive focus on reflection in the Middle Years  (MYP)and Diploma (DP) program implemented at WAB. For example, the posts from DS-12 failed to show a development of metacognitive skills over time; typically, these posts focused mainly on what he had done (self-observation) but failed to report how well he did (self-judgement) nor what he would change as a result (self-reaction). Students found that the blog was cumbersome to use, for example, PJ-12 said, “the blog is hard to keep updated because most as being a photojournalist it falls more into visual learning and don’t know how to put that on paper and prove that”. Similarly, D-12 recorded reflective interviews with his mentors, which he transcribed and uploaded to his blog; however, despite structured questioning techniques, D-12 mainly offered descriptions of their process (self-observation). Overall, students were not motivated to reflect formally on their learning and saw this as a requirement imposed upon them.

On the other hand, informal self-monitoring was evident. Students revealed that they continuously assessed their work although they rarely captured it as explained by PJ-12 who said “in the moment, when I am using my camera I say ‘now I know this”. By far, DA-12 was the most reflective and actively made changes to improve her work, for example when analyzing her artwork, she applied two criteria, time and efficiency, by “latching on to the fast ones to see what made me more efficient”. Nevertheless, she rarely documented the process although she did post all of her work on her personal blog.

Overall, despite students scoring highly (around 4.0) on the SDLA scale for self-monitoring supported by evidence of informal, self-reflective processes, formal reflection of learning was lacking. Reflective practice is an essential part of the learning process and self-directed learners need to be mindful in their reflection process by consistently documenting, revisiting and sharing their progress through digital tools such as blogs (Xie, Ke, & Sharma, 2010; C. Yang & Chang, 2012).

Motivation and Collaboration

The study concurred with the observations from Fahnoe & Mishra (2013) who noted that self-directed learners are driven to search online for resources and learning materials. Students sought out experts and mentors beyond the confines of the school with some participating in online collaborative practices (e.g. DA-12, CS-12). Networking, advice from others and peer collaboration are motivating factors, as well as necessary skills, for a successful learning experience (D. Song & Bonk, 2016; L. Song & Hill, 2007). Although these participatory traits were not explored through the SDLA scale, students reported that collaborative online experiences were highly motivating. For example, a free online MOOC-style digital art course enabled DA-12 to be taught by highly-experienced and famous gaming artists thus providing learning opportunities that are only possible through digital technologies. This course motivated DA-12 to submit high-quality weekly assignments for peer review, a process through which she discovered unexpected sources of inspiration through the work and feedback of others. CS-12 contributed to online forums by offering solutions to other users’ programming problems and was rewarded through a system of user ratings. These findings are supported by Gilbert & Driscoll (2002) who observed that participatory experiences and collaboration traits are important to students’ personal learning and group-based knowledge building.

Freedom to control learning, creating something new and choice are also significant motivating factors in SDL (D. Song & Bonk, 2016), and therefore it was not surprising that the students scored highly on the SDLA scale for this dimension (motivation). On the whole, students reported that they were highly motivated to work on their Capstone projects. They did not consider their Capstone learning as ‘schoolwork’ as they enjoyed the freedom provided by mobile technologies and were motivated to work at times and locations that suited them, for example, as PJ-12 noted, “on the bus and in a café, in school holidays and sometimes during other classes”.

Conclusion and recommendations

Conclusion

Using the Capstone program as a case, the findings of this study determined how, and to what extent, digital tools and appropriate learning strategies might support self-directed learning experiences in high school students. In doing so, this study examined attitudes required of a self-directed learner and the process of SDL using the models put forward by Knowles (1975) and Garrison (1997) and offered insight into the types of challenges and barriers faced by students in using digital tools to support SDL in a high school context.

Most significantly, the study showed that digital tools, resources and mobile technologies had a positive influence on SDL for high school students. Digital tools can support each stage of SDL process (Knowles, 1975) and, if applied intentionally, could enhance and act as a catalyst in promoting the dispositions (Garrison, 1997) of a self-directed learner.

The findings suggested a tension between the students’ ability to transfer and apply skills in new contexts and the students’ motivation to perform the steps in learning cycle that they perceived as low stakes in their learning, such as evaluating their planning process and formally reporting on the assessment of their work. However, in the self-determined stages of learning where Capstone students were able to make most of the decisions regarding what they learned, they were highly motivated and applied highly competent strategies for selection and application of digital tools, for example in the creation of tangible products (Hase & Kenyon, 2000).

Finally, this study showed that SDL requires the learner to be more than being technologically literate and skilled at using digital tools. Learners need to understand the cyclic nature of the learning process and the significance and value of each stage, particularly when planning and assessing learning. As Rashid & Asghar (2016) note,

Although Digital Natives are adept and highly fluent with the practical skills of game playing, social networking, texting, and surfing information on the web, it could not be a reliable indicator that they are making best use of these skills for their academic purposes at all (p. 609).

Recommendations

Schools need to create an atmosphere of SDL to inculcate and develop SDL dispositions in students (Du Toit-Brits & Van Zyl, 2017) and prepare students adequately by acquiring a repertoire of digitally-supported learning strategies (Lee et al., 2014). This may be achieved through the consideration of the following recommendations:

1. Measuring students’ SDL can help surface the problems they are facing (Lee, Tsai, Chai, & Koh, 2014). A recommendation would be to extend the SDLA survey to ascertain a student’s digital readiness for SDL by measuring a student’s level of technology skills in addition to their dispositions. A suggestion would be to use the Delphi technique, by soliciting opinions from experts (Hasson & Keeney, 2011).
2. Schools might consider adopting the ISTE standards for teachers (2017) and students (ISTE, 2017) as a framework for developing training programs for teachers to support students’ use of technology. This technology may include tools to record different versions of ideas, which can enhance learning by encouraging idea generation, recalling and remixing of ideas when the situation required. (Lin, 2008).
3. Students must be encouraged to be active participants, rather than passive recipients of knowledge, by engaging in online social participatory practices. Schools must challenge, motivate and support students to take initiative in pursuing their learning goals in a community of inquiring learners (Foo & Hussain, 2010).
4. Schools should consider the ways of integrating technologies that promote critical thinking and the self-assessment of learning by encourage reflective practices, through blogging or other forms of open reflection that allows for feedback. Moreover, students should consider using tags for their reflective posts to produce a link representative of “a wormhole between [their] experiences and present and someone else’s (which might be a past self).” (Ross, 2012, p. 263).

References

Abd-El-Fattah, S. M. (2010). Garrison’s model of self-directed learning: preliminary validation and relationship to academic achievement. The Spanish Journal of Psychology, 13(2), 586–596. http://doi.org/10.1017/S1138741600002262

Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., … Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Theory Into Practice (Vol. Complete e).

Bartholomew, S. (2016). A mixed-method study of mobile devices and student self-directed learning and achievement during a middle school STEM activity. ProQuest Dissertations and Theses. Utah State University, Ann Arbor.

Baxter, P., & Jack, S. (2008). Qualitative Case Study Methodology : Study Design and Implementation for Novice Researchers Qualitative Case Study Methodology : Study Design and Implementation, 13(4), 544–559.

Brookfield, S. (1985). Self-directed learning: A critical review of research. New Directions for Adult and Continuing Education, 25(March), 5–16. http://doi.org/10.1002/ace.36719852510

Cadorin, L., Cheng, S.-F., & Palese, A. (2016). Concurrent validity of self-rating scale of self-directed learning and self-directed learning instrument among Italian nursing students. BMC Nursing, 15, 20. http://doi.org/10.1186/s12912-016-0142-x

Candy, P. C. (1991). Self-direction for lifelong learning: A comprehensive guide to theory and practice. The Jossey-Bass Higher and Adult Education Series, (1991), 567.

Du Toit-Brits, C., & Van Zyl, C.-M. (2017). Self-directed learning characteristics: making learning personal, empowering and successful. Africa Education Review, 1–20. http://doi.org/10.1080/18146627.2016.1267576

Fahnoe, C., & Mishra, P. (2013). Do 21st Century Learning Environments Support Self-Directed Learning? Middle School Students’ Response to an Intentionally Designed Learning Environment. In R. McBride & M. Searson (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2013 (pp. 3131–3139). New Orleans, Louisiana, United States: Association for the Advancement of Computing in Education (AACE).

Feryal, L., & Selv, K. (2016). Developing a “self -directed learning implementation skills scale for primary school students”: validity and reliability analysis. AGATHOS, 7(1).

Fisher, M. J., & King, J. (2010). The self-directed learning readiness scale for nursing education revisited: A confirmatory factor analysis. Nurse Education Today, 30(1), 44–48. http://doi.org/10.1016/j.nedt.2009.05.020

Foo, S. Y., & Hussain, R. M. R. (2010). Self-directed learning in a socioconstructivist learning environment. Procedia – Social and Behavioral Sciences, 9, 1913–1917. http://doi.org/10.1016/j.sbspro.2010.12.423

Garrison, D. R. (1991). Critical thinking and adult education: A conceptual model for developing critical thinking in adult learners. International Journal of Lifelong Education, 10(4), 287–303. http://doi.org/10.1080/0260137910100403

Garrison, D. R. (1997). Self-Directed Learning: Toward a Comprehensive Model. Adult Education Quarterly, 48(1), 18–33. http://doi.org/10.1177/074171369704800103

Green, H. (2014). Use of theoretical and conceptual frameworks in qualitative research. Nurse Researcher, 21(6), 34–38.

Grow, G. O. (1991). Teaching Learners To Be Self-Directed. Adult Education Quarterly, 41(3), 125–149. http://doi.org/10.1177/0001848191041003001

Guglielmino, L. M. (1977). Development of the Self-Directed Learning Readiness Scale. Disseration. University of Georgia.

Gündüz, G. F., & Selvi, K. (2016). Developing a “self-directed learning implementation skills scale for primary school students”: validity and reliability analysis. Agathos: An International Review of the Humanities and Social Sciences, (1), 103–124.

Hase, S., & Kenyon, C. (2000). From Andragogy to Heutagogy. ultiBASE, 28, 2005.

Hasson, F., & Keeney, S. (2011). Enhancing rigour in the Delphi technique research. Technological Forecasting and Social Change, 78(9), 1695–1704. http://doi.org/10.1016/j.techfore.2011.04.005

Hiemstra, R., & Brockett, R. G. (2012). Reframing the Meaning of Self-Directed Learning: An Updated Model. Proceedings of the 54th Annual Adult Education Research Conference, 155–161.

International Society for Technology in Education (ISTE). (2017). ISTE Standards for Students. Retrieved from https://www.iste.org/standards/for-students

International Society for Technology in Education (ISTE). (2017). ISTE Standards for Teachers. Retrieved from https://www.iste.org/standards/for-teachers

Lee, K., Tsai, P.-S., Chai, C. S., & Koh, J. H. L. (2014). Students’ perceptions of self-directed learning and collaborative learning with and without technology. Journal of Computer Assisted Learning, 30(5), 425–437. http://doi.org/10.1111/jcal.12055

Oddi, L. F. (1987). Perspectives On Self-Directed Learning. Adult Education Quarterly, 38(1), 21–31. http://doi.org/10.1177/0001848187038001003

Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604–612. http://doi.org/10.1016/j.chb.2016.05.084

Robertson, J. (2011). The educational affordances of blogs for self-directed learning. Computers and Education, 57(2), 1628–1644. http://doi.org/10.1016/j.compedu.2011.03.003

Ross, J. (2012). The spectacle and the placeholder: digital futures for reflective practices in higher education. {…} of the 8th International Conference on {…}, 260–265.

Saks, K., & Leijen, Ä. (2014). Distinguishing Self-directed and Self-regulated Learning and Measuring them in the E-learning Context. Procedia – Social and Behavioral Sciences, 112(Iceepsy 2013), 190–198. http://doi.org/10.1016/j.sbspro.2014.01.1155

Song, D., & Bonk, C. J. (2016). Motivational factors in self-directed informal learning from online learning resources. Cogent Education, 3(1), 1–11. http://doi.org/10.1080/2331186X.2016.1205838

Song, L., & Hill, J. R. (2007). A Conceptual Model for Understanding Self-Directed Learning in Online Environments. Journal of Interactive Online Learning, 6(1), 27–42.

Teo, T., Tan, S. C., Kee, C. B., Chai, C. S., Koh, J. H. L., Chen, W. L., & Cheah, H. M. (2010). The self-directed learning with technology scale (SDLTS) for young students: An initial development and validation. Computers & Education, 55(4), 1764–1771. http://doi.org/10.1016/j.compedu.2010.08.001

Xie, Y., Ke, F., & Sharma, P. (2010). The Effects of Peer-Interaction Styles in Team Blogs on Students’ Cognitive Thinking and Blog Participation. Journal of Educational Computing Research, 42(4), 459–479. http://doi.org/10.2190/EC.42.4.f

Yang, C., & Chang, Y. S. (2012). Assessing the effects of interactive blogging on student attitudes towards peer interaction, learning motivation, and academic achievements. Journal of Computer Assisted Learning, 28(2), 126–135. http://doi.org/10.1111/j.1365-2729.2011.00423.x

Yang, Y.-F. (2015). Self-directed learning to develop autonomy in an online ESP community. Interactive Learning Environments, 4820(June), 1–18. http://doi.org/10.1080/10494820.2015.1041402

 

Appendix A

Statements from the Self-Directed Learning Aptitude Scale (SDLAS)

The following lists the statements from the SDLAS organised into three dimensions. Students were asked to rate each question using 5-point Likert scale, ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5).

 

1. I enjoy learning new things.	Motivation
2. I take the challenge to learn.	Motivation
9. I believe in effort to improve my performance.	Motivation
11. I critically evaluate new ideas and knowledge.	Motivation
12. I have positive expectations about what I am learning.	Motivation
15. I am a ‘why’ person.	Motivation
17. I trust my abilities to learn new things.	Motivation
22. I would like to evaluate the level of my learning progress.	Motivation
26. I would like to learn from my mistakes.	Motivation
3. I can decide about the priority of my work.	Self-management
5. I prefer to plan my own learning.	Self-management
7. I set up planned solutions to solve my problems.	Self-management
8. I set up strict timeframes to learn something new.	Self-management
10. I am well-organized in my learning.	Self-management
14. I have good management skills.	Self-management
18. I am efficient in managing my time.	Self-management
20. I can manage pursuing my own learning.	Self-management
4. I can link pieces of information when I am learning.	Self-monitoring
6. I am aware of my own weaknesses.	Self-monitoring
13. I pay attention to all details before taking a decision.	Self-monitoring
16. I prefer to set up my criteria to evaluate my performance.	Self-monitoring
19. I would like to set up my goals.	Self-monitoring
21. I judge my abilities fairly.	Self-monitoring
23. I correct myself when I make mistakes.	Self-monitoring
24. I think deeply when solving a problem.	Self-monitoring
25. I am a responsible person.	Self-monitoring

 

 

Appendix B

Example of Defense Report

Student:

Area of Focus: Art in Computer Gaming

Date:

Academic Panel:

HS Principal, Deputy Director, HOD Counselling, Teacher of Record, HS Art, /HS Curriculum Coordinator

Purpose:

For student to be awarded credit for their Semester 2 journey by focusing on these areas:

• Growth in learning
• Quality and significance of learning
• Reflection of themselves as a learner

For the academic panel to offer guidance through commendations, requirements and recommendations. Requirements must be addressed in the following semester; recommendations must be considered.

Format:

45 minutes: Presentation of learning by student and Q&A session with panel. The student is required to cover the following elements and supported by evidence of learning in their online blog:

1. Address the expectations and recommendations from the academic panel from Semester 1 Defence meeting;
2. Summary of assessment artefacts;
3. Reflection on goals for Semester 2;
4. Approaches to Learning;
5. Thinking Ahead.

15 minutes: Panel (only) discuss and outline commendations, requirements and recommendations.

Outcome:  – has successfully demonstrated her learning

Commendations:

We commend – on the following:

1. Challenging * to work faster as evidence in the large body of art created this semester;
2. Continued documentation of learning journey in the blog;
3. Success with the *** project, from creating an excellent logo and meeting deadlines.

Expectations:

For semester 3, the academic panel has the following expectations:

• Determine your plans after graduating from WAB and develop a portfolio/portfolios of work targeted for these plans;
• Continue to focus on drawing human figures and clearly documenting your progress using a methodical and organised method;
• Continue to cultivate relationships with mentors in school, in Beijing and beyond and seek out authentic challenges/commissions;
• Work with mentors in school to develop communication skills for preparation for college interviews and working with clients.

Recommendations:

The academic panel highly recommends the following:

1. Take up the internship offered over the summer as this may lead to more diverse design projects and may lead to contacts/connections in the areas you are pursuing.
2. Clearly articulate your goals for your future as this will help you determine the type of work you will undertake for your portfolio.
3. Document your work to show how the development from early stages to the (completed) piece.
4. Continue with developing your technology skills, particularly with Illustrator and Photoshop as well as organising your digital files in order to streamline your workflow.

Please note that:

• You will be given guidance on how to address the expectations and recommendations;
• Expectations and recommendations must be addressed in Semester 3 Defense.
• Projects for other subjects, e.g. IB ITGS, cannot be used as evidence.