1 Personalized learning using ICTS
Tyler Reid, Memorial University of Newfoundland
Learners’ needs and classrooms are becoming increasingly diverse across North America and around the world (Howery, McClellan, & Pedersen-Bayus, 2013). 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 or learning management systems that do not allow learners’ to set personal goals and connect with peers (Dabbagh & Kitsantas, 2012). This type of linear learning contradicts with knowledge about how students actually learn (Hurson & Sedigh, 2010). Furthermore, many standardized distance courses limit and restrict personal creativity and empowerment (Bidarra & Araujo, 2013). Creating a PLE in a face-to-face environment is challenging because 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” approach to student assessment, many learners never feel successful because they are constantly compared to their peers (Yalcinalp & Gulbahar, 2010). Impersonalized learning can negatively affect learners’ attitudes because they do not relate to the content being studied (Howery et al., 2013).
3 Role of ICTs
Using the internet or e-learning is one approach than can make learning more personalized (Abik & Ajhoun, 2009). More specifically, Chen (2008) developed a genetic-based personalized e-learning system which allows learners to progress through 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). Learners 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 specific 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 online at a personalized pace was the most significant advantage described by middle school students in one study (Edwards & Rule, 2013). Other key benefits included the ability to start and stop videos when experiencing difficulties, to collaborate with peers, and to easily access online resources. Additionally, students expressed how they were not limited by time or space. Personalized online learning can be enhanced with virtual change agents; these human-like animated characters provide encouragement and suggest strategies when learners encounter difficult tasks (Kim, 2012). Furthermore, personalized learning can be achieved and aligns with the emergent popularity of 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). Using e-books supports personalized learning because students can search difficult words online and make personal notes while reading (Huang, Liang, Su, & Chen, 2012). In another study, students received smartphones to document an excursion 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 collaboration in an authentic context. Mobile devices increase learning time for individuals because they can login anytime and anywhere (Nedungadi & Raman, 2012). Education combined with mobile technology enhances personalization because students “can take their learning to go” (McElvaney & Berge, 2009, p. 7).
There is concern that personalized learning is ineffective for students who lack self discipline (Edwards & Rule, 2013). Students may initially resist PLEs because they “entail more complexity and work for the learner” (Johnson & Liber, 2008, p. 12). In some cases, PLEs cause anxiety and cognitive overload among learners (Chen, 2009). Furthermore, students may not have the necessary self-regulation or knowledge management skills which allow them to successfully customize a personal learning environment (Bidarra & Araujo, 2013). However, encouraging learners to use social media, such as wikis or blogs, can assist the development of personal knowledge and self-regulation skills through creative collaboration and critical thought (Dabbagh & Kitsantas, 2012). Successful PLEs require a re-examination of teacher and student roles in the educational setting in order for students to become more autonomous (Johnson & Liber, 2008).
Many PLEs currently being used by institutions lack elements of social connectivity, and are often controlled by instructors (McLoughlin & Lee, 2010). Virtual learning environments used on a large scale are difficult to customize and evolve slowly which does not satisfy students seeking autonomous learning (Severance, Hardin, & Whyte, 2008). Web 2.0 tools can be used to create PLEs and provide students with a high degree of choice and control (Tomberg, Laanpere, Ley, & Normak, 2013). Additionally, specialized software, like LePress, can help instructors maintain some control while providing students a flexible PLE (Tomberg et al., 2013). Finally, PLEs through social media should encourage learner-generated content, personal reflection, social connections, while capitalizing on pre-existing skills and resources outside of the institution (McLoughlin & Lee, 2010).
5 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.
Bidarra, J. & Araujo, J. (2013). Personal learning environments (PLEs) in distance learning course on mathematics applied to business. European Journal of Open, Distance and e-Learning, 16(2), 141-152.
Chen, C. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2), 787-814.
Chen, C. (2009). Ontology-based concept map for planning a personalised learning path. British Journal of Educational Technology, 40(6), 1028-1058.
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.
Dabbagh, N. & Kitsantas, A. (2012). Personal Learning Environments, social media, and self- regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3-8.
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.
Hurson, A. & Sedigh, 2010. PERCEPOLIS: Pervasive cyberinfrastructure for personalized learning and instructional support. Intelligent Information Management, 2, 586-596.
Johnson, M. & Liber, O. (2008). The personal learning environment and the human condition: From theory to teaching practice. Interactive Learning Environments, 16(1), 3-15.
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), 1-12.
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.
Severance, C., Hardin, J., & Whyte, A. (2008). The coming functionality mash-up in personal learning environments. Interactive Learning Environments, 16(1), 47-62.
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.
Tomberg, V., Laanpere, M., Ley, T., & Normak, P. (2013). Sustaining teacher control in a blog- based personal learning environment. The International Review of Research in Open and Distance Learning, 14(3), 109-133.
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.