1 Supporting At-risk Learners using ICTs
Armel Boudreau, Memorial University of Newfoundland
At-risk learners have difficulty engaging in academic studies in a typical classroom environment (Tay & Lim, 2008). In this environment, instructional time was usually spent on drills and practice or group lectures (Diem & Katims, 2002) and assessments were based on “high stakes paper and pencil tests” (Tay & Lim, 2010, p.521). Diem and Katims (2002) found that “most teachers and, in fact, the educational system as a whole, operate under the supposition that at-risk learners must be taught basic skills in discrete segments–often to the exclusion of more interesting and useful skills” (p.20). Li and Edmonds (2005) posit that traditional educational practices were ineffective for at-risk learners.
Teacher directed instruction leads to disinterest, boredom, frustration, behavioral issues and disengagement of at-risk learners which, in turn, decreases their performance and self-confidence (Kajander, Zuke & Walton, 2008). Traditional classrooms are geared toward the autonomous learner and this type of classroom structure creates a challenge for at-risk learners because they tend to be disorganized and unmotivated (Griff & Matter, 2008). Moreover, the integration of collaborative learning in a typical classroom is difficult for at-risk learners because they are not wanted in groups and because high achievers prefer to work individually (Samsonov, Pedersen & Hill, 2006).
Kemker, Barron and Harmes (2007) maintain that a lack of access to technology negatively affects at-risk learners. Leroy and Symes (2001) cited the lack of parental involvement, domestic problems, low literacy rates, an adversarial school system, and teachers’ inability to identify at-risk learners as contributing factors to this problem. Some at-risk learners have academic deficiencies while others have average or above average academic capabilities but display significant behavioral problems and, as a result, this created a complex and multidimensional problem for their teachers (Samsonov et al., 2006).
3 Role of ICTs
Schools are working toward improving achievement through varied uses of instructional technology (Flumerfelt & Green, 2013). Middle and high school students following the Make It-Take It After-School Case Study computer integration system acquired hands-on experience, trouble shooting skills and had greater improvements in reading and academic performance (Amiri, 2009). Moreover, Woodul, Vitale & Scott (2000) concluded that the integration of cooperative multimedia learning activities improved the academic achievement and self-perceptions of at-risk grade 8 learners. Similarly, Li and Edmonds (2005) demonstrated that Computer Assisted Instruction is an effective method for improving mathematical skills and achievement, confidence and the satisfaction level of at-risk learners of various skill levels. Finally, Kemker et al. (2007) confirmed that the integration of laptop computers in a classroom of at-risk learners improved motivation and had a positive impact on learner achievement. Kemker et al. (2007) asserted that “the results show authentic tasks and technology are a feasible combination for at-risk students in elementary school” (p.318).
Griff and Matter (2008) studied the relationship between clicker technology and the early identification of at-risk learners and the authors ascertained that technological clickers can quickly identify at-risk learners in a classroom because class members who register their clickers last usually struggle academically. Armed with this information, the teacher can initiate a dialogue through e-mail or in person to offer encouragement and support and this interaction often provides sufficient motivation for at-risk learners to perform at a higher level (Griff & Matter, 2008).
Student motivation in academic-related tasks is enhanced in the virtual learning environment through the use of avatars, cols, digital artifacts and reward systems (Tay & Lim, 2010). Also, motivation is increased, discipline events and failure rates are reduced and homework rates increased in a flipped classroom using screencast video technology (Flumerfelt & Green, 2013). At-risk learners who are required to apply their knowledge by constructing multimedia presentations were more engaged because they developed a sense of ownership and improved cooperative learning strategies (Woodul et al., 2000). Additionally, Tay and Lim (2008) asserted that implementing ICT quests greatly motivated students to improve their academic learning, harness social responsibility and reduce behavioral problems.
Kinzie, Whitaker, Neesen, Kelley, Matera & Pianta (2006) described the importance of on-going professional development for teachers of at-risk learners. The authors implemented My Teaching Partner, an online professional development platform for 235 teachers of at-risk learners and concluded that teachers of at-risk learners employed more effective and efficient methodologies aimed at improving language, literacy, and social relationships of at-risk learners (Kinzie et al., 2006).
Technology does not improve the academic achievement of at-risk learners unless it is part of a comprehensive technology plan focused on effectively integrating technology into the classroom (Diem & Katims, 2002). Tay and Lim (2008) contend that classrooms can also become obstacles and they confirm that after school programming was a more conducive environment for an effective integration of technology for at-risk learners because the school setting, timetables, scheduling and curriculum delivery requirements were constraining factors. Finally, Barbour and Siko (2012) confirmed that attributing extension activities or homework is also very challenging because “At-risk students often come from poorer households and may not have the necessary resources to provide the level of technology needed for a student to work on asynchronous material anywhere but at the school” (p.10).
Many at-risk learners have limited literacy skills and an inability to understand written language in a technological environment and they also struggle in a student-centered environment because they do not have sufficient skills to internalize the learning strategies (Li and Edmonds, 2005). Moreover, at-risk learners are inefficient at using word processing programs due to limited typing skills and they display a lack of fine motor skills when using the computer’s trackpad (Kemker et al., 2007).
Despite an attempt to have teachers implement technology and engaging reform-based methodologies, they generally fell back into the pattern of teacher-directed instruction which perpetuated the cycle of boredom and frustration for at-risk learners (Kajendar et al., 2008). A greater support staff is needed because even after providing professional development, teachers still required significant support to address their diverse array of technical problems (Kinzie et al., 2006). Furthermore, teachers were unable to effectively identify at-risk learners in their classrooms causing the incidence and severity of problems to escalate and become harder to address (Leroy & Symes, 2001).
5 Works cited
Amiri, S. (2009). The effects of information and communication technology on at risk children of low economic status: Make it-take it case study. International Journal of Education and Development using Information and Communication Technology, 5(3), 141–147.
Barbour, M. & Siko, J. (2012). Virtual schooling through the eyes of an at-risk student: A case study. European Journal of Open, Distance and E-Learning, 1, 1-14.
Diem, R. & Katims, D. (2002). The introduction of computers in an at-risk learning environment: A seven-year retrospective view. Computers in the Schools, 19(1). 19-32.
Flumerfelt, S., & Green, G. (2013). Using lean in the flipped classroom for at risk students. Educational Technology & Society, 16(1), 356–366.
Griff, E. & Matter, S. (2008). Early identification of at-risk students using a personal response system. British Journal of Educational Technology, 39(6), 1124–1130.
Hasselhorn, M., Linke-Hasselhorn, K. (2013). Fostering early numerical skills at school start in children at risk for mathematical achievement problems: A small sample size training study. International Education Studies, 6(3), 213-220.
Kajander, A., Zuke, C. & Walton, G. (2008). Teaching unheard voices: Students at-risk in mathematics. Canadian Journal of Education, 31(4). 1039-1064.
Kemker. K., Barron, A. & Harmes, C. (2007). Laptop computers in the elementary classroom: Authentic instruction with at-risk students. Educational Media International, 44(4), 305-321.
Kinzie, M., Whitaker, S., Neesen, K., Kelley, M., Matera, M., & Pianta, R. C. (2006). Innovative web-based professional development for teachers of at-risk preschool children. Educational Technology & Society, 9(4), 194-204.
Leroy, C. & Symes, B. (2001). Teachers' perspectives on the family backgrounds of children at risk. McGill Journal of Education. 36(1), 45-60.
Li, Q. & Edmonds, K. (2005). Mathematics and at-risk adult learners: would technology help? Journal of Research on Technology in Education, 38(2), 143-166.
Samsonov, P., Pedersen, S. & Hill, C. (2006). Using problem-based learning software with at-risk students: A case study. Computers in the Schools, 23(1), 111-124.
Tay, L. & Lim, C. (2008). Engaging academically at risk primary school students in an ICT mediated after school program. Australasian Journal of Educational Technology, 24(5), 521-539.
Tay, L. & Lim, C. (2010). An activity theoretical perspective towards the design of an ICT-enhanced after-school programme for academically at-risk students. Educational Media International, 47(1), 19-37.
Woodul, C., Vitale, M. & Scott, B. (2000). Using a cooperative multimedia learning environment to enhance learning and affective self-perceptions of at-risk students in Grade 8. Journal of Educational Technology Systems, 28'Italic text(3). 239-252.