Personalized learning

Position paper on ICT technology to foster personalized learning

Tyler Reid, Memorial University of Newfoundland

Problem

Learners’ needs and classrooms are becoming increasingly diverse across North America (Howery, McClellan, & Pedersen-Bayus, 2013), Australia (Jones & McLean, 2012) and Europe (Tezci, 2011). As a result, “instruction needs to be specialized to meet the needs of each learner” (Howery et al., 2013, p. 294). Teachers are facing resistance from 21st century students who are not satisfied being passive recipients of static knowledge; conversely, they are seeking greater involvement related to decision making in a dynamic and participatory learning environment (Jones & McLean, 2012). Despite student demands, many universities still rely on traditional lectures where the professor disseminates specific content in a given period of time (Sana, Fenesi, & Kim, 2011). Creating a personalized learning environment in a face-to-face environment is challenging for several reasons; firstly, many university classes around the world exceed one hundred students which makes interaction very difficult (Mompo & Redoli, 2010), secondly, many teachers are unprepared to deal with diverse learning needs (Jones & McLean, 2012). Additionally, traditional classrooms are limited by space and time which leads to instructors simply covering the core content (Abik & Ajhoun, 2009). Finally, when instructors use a “one-size-fits-all” (Yalcinalp & Gulbahar, 2010, p. 885) approach to student assessment, many learners never feel successful because they are constantly compared to their peers (Jones & McLean, 2012). Impersonalized learning can negatively affect learners’ attitudes because they do not relate to the content being studied (Howery et al., 2013).

Role of ICTs

To assist teachers in creating personalized learning environments, huge amounts of money are being invested on ICT infrastructure (Tezci, 2011). However, investing in technology does not guarantee teachers will use it effectively (Tezci, 2011, p. 483). This study highlighted the need for effective professional training among pre-service teachers because many lacked self-confidence and a positive attitude towards ICT integration. This preparation is critical because the internet, or e-learning (Chen, 2008), can be used to make learning more personalized (Abik & Ajhoun, 2009). For example, Chen (2008) developed a genetic-based personalized e-learning system which allows learners to progress through a sequence of online tutorials and activities at a personal pace. Specific needs are targeted with this tool because the database provides questions based on previous errors, therefore time is not wasted on concepts already mastered. Additionally, the instructor has access to student progress which facilitates their ability to provide personalized feedback. In another study, a computer-assisted program was used to personalize math problems from a textbook by inserting familiar names from a particular group of students (Chen & Liu, 2007). Students connected with the personalized word problems and this process assisted their reading comprehension. Similarly, creating online student profiles to set learning goals is an essential step when using the e-learning INDeLER system (Jovanovic, Milosevic, & Zizovic, 2008). This program considers learning styles, prior knowledge, and “can be adapted to the individual learner, which is hard to achieve in the common teaching process” (p. 41).

Working at a personal pace online was the most significant advantage described by middle school students in one study (Edwards & Rule, 2013). Other key benefits were the ability to start and stop videos, and the availability of online resources. Additionally, students expressed how they were not limited by time or space. Working online can also be personalized with virtual change agents; these human-like animated characters provide encouragement and suggest strategies when learners encounter difficult tasks (Kim, 2012). Furthermore, personalization aligns nicely with Web 2.0 sites that favour user-generated content and collaboration (McLoughlin & Lee, 2010). In this context, learners have choices because tasks are “controlled by the individual, not the institution” (Hall, 2009, p. 33).

E-learning can be further personalized with mobile devices, or m-learning (Faulkner, 2013). Mobile devices help teachers meet specific needs of students, for example, learners with visual impairments can easily resize text (Faulkner, 2013). In another study, students received smartphones to document a field trip and complete scientific research (Song, Wong, & Looi, 2012). Students found the experience highly personal because the devices provided a sense of control and ownership. Finally, the smallness and mobility of the devices encouraged sharing and allowed students to collaborate in an authentic context. Education combined with mobile technology is highly personal because students “can take their learning to go” (McElvaney & Berge, 2009).

Obstacles

The issue of cost is always an obstacle when dealing with ICT technologies (Abik & Ajhoun, 2009). However, the price of mobile phones is decreasing and may provide a cheaper alternative for schools who cannot invest in computer labs (Nedungadi & Raman, 2012). Many schools are adopting a BYOD (bring your own device) policy (Faulkner, 2013) which reduces the financial burden for schools because many students already own a mobile phone or tablet (Nedungadi & Raman, 2012). Regarding e-learning, there are many free technologies readily available online (McElvaney & Berge, 2009).

Some researchers argue that e-learning or m-learning can lead to students becoming distracted (Huang, Liang, Su, & Chen, 2012) due to the open nature of the internet (Chen, 2009). Although this is possible in some cases, the majority of students are more motivated and engaged when using personalized learning tools (Nedungadi & Raman, 2012; Chen & Liu, 2007). Setting up class groups for support (McLoughlin & Lee, 2010), designing a central website (Edwards & Rule, 2013), and structuring specific learning paths can keep students focused online while reducing the risk of cognitive overload (Chen, 2009).

There is concern that personalized learning is not effective for students who lack self discipline (Edwards & Rule, 2013). However, teachers can monitor progress online more closely when activities and discussions are documented (Chen & Liu, 2007). Online student profiles allow the instructors to teach each student exclusively (Jovanovic, Milosevic, & Zizovic). Kim (2012) also highlighted how providing timely feedback online can address students who lack discipline.

Works cited

Abik, M. & Ajhoun, R. (2009). Normalization and personalization of learning situations: NPLS. International Journal of Emerging Technologies in Learning (iJET), 4(2), 4-10.

Chen, C. (2009). Ontology-based concept map for planning a personalised learning path. British Journal of Educational Technology, 40(6), 1028-1058.

Chen, C. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2), 787-814.

Chen, C. & Liu, P. (2007). Personalized computer-assisted mathematics problem-solving program and its impact on Taiwanese students. Journal of Computers in Mathematics and Science Teaching, 26(2), 105-121.

Edwards, C. & Rule, A. (2013). Attitudes of middle school students: Learning online compared to face to face. Journal of Computers in Mathematics and Science Teaching, 32(1), 49-66.

Faulkner, R. (2013). Schools going mobile: A study of the adoption of mobile handheld technologies in Western Australian independent schools. Australasian Journal of Educational Technology, 29(1), 66-81.

Hall, R. (2009). Towards a fusion of formal and informal learning environments: The impact of the read/write web. Electronic Journal of e-Learning, 7(1), 29-40.

Howery, K., McClellan, T., & Pedersen-Bayus, K. (2013). “Reaching every student” with a pyramid of intervention approach: One district’s journey. Canadian Journal of Education, 36(1), 271-304.

Huang, Y., Liang, T., Su, Y., & Chen, N. (2012). Empowering personalized learning with an interactive e-book learning system for elementary school students. Educational Technology Research and Development, 60(4), 703-722.

Jones, M. & McLean, K. (2012). Personalising learning in teacher education through the use of technology. Australian Journal of Teacher Education, 37(1), 75-92

Jovanovic, D., Milosevic, D. & Zizovic, M. (2008). INDeLER: eLearning personalization by mapping student’s learning style and preference to metadata. International Journal of Emerging Technologies in Learning (iJET), 3(4), 41-50.

Kim, C. (2012). The role of affective and motivational factors in designing personalized learning environments. Educational Technology and Research Development, 60(4), 563-584.

McLoughlin, C., & Lee, M. (2010). Personalised and self regulated learning in the web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 28-43.

McElvaney, J. & Berge, Z. (2009). Weaving a personal web: Using online technologies to create customized, connected, and dynamic learning environments. Canadian Journal of Learning and Technology, 35(2).

Mompo, R., & Redoli, J. (2010). Some internet-based strategies that help solve the problem of teaching large groups of engineering students. Innovations in Education and Teaching International, 47(1), 95-102.

Nedungadi, P., & Raman, R. (2012). A new approach to personalization: Integrating e-learning and m-learning. Educational Technology Research and Development, 60(4), 659-687.

Sana, F., Fenesi, B. & Kim, J. (2011). A Case study of the introductory psychology blended learning model at McMaster University. Canadian Journal for the Scholarship of Teaching and Learning, 2(1).

Song, Y., Wong, L., & Looi, C. (2012). Fostering personalized learning in science inquiry supported by mobile technologies. Educational Research and Development, 60(4), 679-701.

Tezci, E. (2011). Factors that influence pre-service teachers’ ICT usage in education. European Journal of Teacher Education, 34(4), 483-499.

Yalcinalp, S., & Gulbahar, Y. (2010). Ontology and taxonomy design and development for personalised web-based learning systems. British Journal of Educational Technology, 41(6), 883-896.