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How can self-regulated learning be supported in mathematical E-learning environments?

How can self-regulated learning be supported in mathematical E-learning environments?. Presenters: Wei-Chih Hsu Professor : Ming-Puu Chen Date : 11/10/2008.

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How can self-regulated learning be supported in mathematical E-learning environments?

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  1. How can self-regulated learning be supported in mathematical E-learning environments? Presenters: Wei-Chih Hsu Professor : Ming-Puu Chen Date : 11/10/2008 B. Kramarski, ＆M. Gutman (2006). How can self-regulated learning be supported in mathematical E-learning environments?. Journal of Computer Assisted Learning, 22 (1), 24-33.

  2. Introduction(1/2) • This study compares two E-learning environments. • E-learning supported with IMPROVE self-metacognitive questioning (EL+IMP), • E-learning without explicit support of self-regulation (EL). • The effects were compared • Mathematical problem-solving, • Self-regulated learning (SRL). • Research has focused on students’ SRL skills in addition to subject matter knowledge for the successful acquisition of knowledge in school.

  3. Introduction(2/2) • The purpose of the study is threefold • (a) to investigate the ability to solve procedural tasks of students who were exposed either to the EL+IMP or EL instructional approach; • (b) to examine the ability to solve transfer tasks regarding mathematical explanations of students who were exposed to these instructional approaches; • (c) to compare the differential effects of both approaches on SRL regarding strategy use and self monitoring.

  4. Literature review (1/4) • Students are self-regulated to the degree that they are metacognitively, motivationally, and behaviourally active participants in their own learning process(Zimmerman & Schunk,2001; Zimmerman 1998; PISA 2003). • Programme for International Student Assessment (PISA) describes SRL as a style of activities for problem solving that includes three phases: • (1) Analysing tasks and setting goals; • (2) Thinking of strategies and choosing the most appropriate strategy for solving the problem; • (3) Monitoring and controlling behaviours, cognitions, and motivations by enlisting strategies such as attention control, encoding control, and self-instruction.

  5. Literature review (2/4) • The findings indicate that there are relationships between students’ SRL processes and academic achievement(e.g. Pintrich & De Groot 1990; Zimmerman & Martinez-Pons 1990; Schoenfeld 1992;Mevarech & Kramarski 1997; Zimmerman 1998;Zimmerman & Schunk 2001; Kramarski & Mevarech 2003; PISA 2003; Azevedo & Cromley 2004; Butler& Cartier 2005). • Students need to have both the will and the skill to be successful in classrooms, and we need to integrate these components in our models of classroom learning (Pintrich & Groot 1990, p. 38). • Despite the importance of SRL in learning, research indicated that learners have difficulties in SRL behaviour.(e.g. Kramarski & Mevarech 2003; Veenman 2005).

  6. Literature review (3/4) • Mathematical problem solving refers to the ability to solve procedural and transfer tasks and to explain mathematical reasoning(NCTM 2000; PISA 2003). • Making disciplinary strategies explicit in E-learning tools can help students think about the steps of the solution process and monitor strategies that they need to adopt in their work (e.g. Collins 1996; Azevedo & Cromley 2004; Quintana et al. 2004; Kramarski & Mizrachi in press). • SRL models include a description of what, how, and why students select a specific self-regulatory strategy, approach, response or explanation within learning (e.g. Palincsar & Brown 1984; Mevarech & Kramarski 1997; Kramarski & Mevarech 2003; Azevedo & Cromley 2004; Butler & Cartier 2005).

  7. Literature review (4/4) • IMPROVE supports students’ SRL in mathematics (Mevarech & Kramarski 1997; Kramarski & Mevarech 2003) • Using four categories of self-metacognitive questioning • (a) comprehending the problem(e.g. ‘What is the problem all about?’); • (b) constructing connections between previous and new knowledge (e.g. ‘What are the similarities/differences between the problem at hand and the problems you have solved in the past? and WHY?’); • (c) using appropriate strategies for solving the problem (e.g. ‘What are the strategies/tactics/principles appropriate for solving the problem and WHY?’; • (d) reflecting on the processes and the solution (e.g. ‘What did I do wrong here?’; ‘Does the solution make sense?’).

  8. Method (1/4) • Participants • 65 (boys and girls) ninth-grade students (age means = 14.5) • They studied in two classes within one junior high school in central Israel. • Instructions • The study utilized two kinds of instructions: general and E-learning instructions. • Measurements • The study utilized two measures for the pre-test and posttest • (a) Mathematical test; • (b) SRL questionnaire.

  9. Method (2/4) • (a) Mathematical Test • A 33-item test about the linear function unit, includes three components. • 12-item: problem solving of procedural tasks. • 14-item: problem solving of transfer tasks. • 7-item: to providing mathematical explanations. • Scoring • The procedural and transfer tasks • 0 point: not responding, or wrong response. • 1 point: correct answer. • The mathematical explanations were scored as a sum of the correct explanations (0–7). • Be analysed based on three criteria of arguments • Mathematical arguments (e.g. formal or daily arguments); • Procedural arguments (e.g. calculation example); • No arguments (e.g. repetition of the question in other words).

  10. Method (3/4) • (b) SRL questionnaire • A 24-item questionnaire assessed SRL processes.--based on the questionnaires of Montague and Bos (1990) and Kramarski and Mevarech (2003). • The SRL questionnaire included two types of SRL processes. • 14 items: the use of problem-solving strategies. • The problem-solving strategies referred to strategies regarding linear function • before solving a problem I try to understand the data in the task; • when I am asked to find the slope, at first, I refer to specific points on the graph. • 10 items: self-monitoring strategies. • self-monitoring strategies refer to control of the solution process • after solving the task, I check the solution if it makes sense; • I try to use different representations to present my solution. • Scoring • Each item was scored on a 5-point Likert scale ranging from completely disagree (1) to fully agree (5).

  11. Method (4/4) • E-learning environments • E-learning supported with the IMPROVE method (EL+IMP), n=35 • (a) self-metacognitive questioning; • Four categories: comprehension, connection, strategic, and reflection. • (b) mathematical explanations; • Provide explanations for the solutions, reflect on their explanations, and suggest ways to modify them. • (c) E-learning metacognitive feedback. • E-learning environment (EL), n=30 • Students were not exposed explicitly to self-regulation activities; • The teacher discussed methods of providing mathematical explanations.

  12. Result (1/3) • The results showed that students exposed to the IMPROVE self-metacognitive questioning in E-learning (EL+IMP) significantly outperformed the EL students in • Problem solving (procedural and transfer tasks) • Mathematical explanations, in particular for providing mathematical arguments. • The EL+IMP students outperformed their peers in using self monitoring strategies but not in the use of problem-solving strategies.

  13. Result (2/3)

  14. Result (3/3)

  15. Discussion & Conclusion(1/2) • There are possible reasons for the beneficial effect of EL+IMP. • Help students access and interact with the content functionality, think about the deeper concepts and structure of disciplinary relations, and avoid superficial details. • Had a cognitive effect on students’ mathematical reasoning and their ability to promote transfer of new knowledge (e.g. Kramarski et al. 2001, 2002). • Our findings strengthen other research conclusions • SRL is teachable, and that students who were exposed to SRL support had more knowledge about self-judging. • These studies noted that meta-cognitive knowledge was positively related to academic performance(e.g. Zimmerman 1998; Schoenfeld 1992; Masui & De Corte 1999; Kramarski et al. 2001; Kramarski & Mevarech 2003).

  16. Discussion & Conclusion(2/2) • Our findings strengthen recommendations for supporting SRL as a vehicle for learning in mathematics instruction. • SRL components can be examined in different ways through observations, interviews, and ‘thinking aloud’ techniques (Veenman 2004). • Further research is needed to investigate questions • Which component of SRL works best for which type of student?; • What is the role of the task in developing SRL skills?; • How can meta-cognitive questioning be presented adaptively under different EL-learning environments?

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