How can self regulated learning be supported in mathematical e learning environments
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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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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.


  • 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.


  • 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.

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.

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).

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).

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?’).

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.

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).

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).

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.

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.

Result (2/3)

Result (3/3)

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).

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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