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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.

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Presentation on theme: "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."— Presentation transcript:

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

9 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 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 11 Method (4/4) E-learning environmentsE-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 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 13 Result (2/3)

14 14 Result (3/3)

15 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 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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