International Journal of Science Education Vol 27, No. 8, 17 June 2005, pp. 957–983
RESEARCH REPORT
The effects of metacognitive instruction embedded within an asynchronous learning network on scientific inquiry skills Michal Zion*, Tova Michalsky and Zemira R. Mevarech Bar-Ilan University, Israel MichalZion 0Taylor 27 School 000002005 & ofArticle Francis EducationBar-Ilan Ltd UniversityRamat–Gan
[email protected] International 10.1080/09500690500068626 TSED106845.sgm 0950-0693 Original 2005 and (print)/1464-5289 Francis Journal Ltd of Science (online) Education
The study is aimed at investigating the effects of four learning methods on students’ scientific inquiry skills. The four learning methods are: (a) metacognitive-guided inquiry within asynchronous learning networked technology (MINT); (b) an asynchronous learning network (ALN) with no metacognitive guidance; (c) metacognitive-guided inquiry embedded within face-to-face (F2F) interaction; and (d) F2F interaction with no metacognitive guidance. The study examined general scientific ability and domain-specific inquiry skills in microbiology. Participants were 407 10thgrade students (15 years old). The MINT research group significantly outperformed all other research groups, and F2F (group d) acquired the lowest mean scores. No significant differences were found between research groups (b) and (c). MINT makes significant contributions to students’ achievements in designing experiments and drawing conclusions. The novel use of metacognitive training within an ALN environment demonstrates the advantage of enhancing the effects of ALN on students’ achievements in science.
Introduction The nature of inquiry learning Recent reforms in science education in the USA emphasize the importance of enhancing high school students’ scientific thinking and knowledge (American Association for the Advancement of Sciences, 1993; National Research Council [NRC], 1996). To enhance scientific thinking and knowledge, the reforms recommend teaching science using an inquiry approach, a similar approach to that by which science is constructed. Science educators have assumed that working on authentic science research projects *Corresponding author. School of Education, Bar-Ilan University, Ramat-Gan 52900, Israel. Email:
[email protected] ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/05/080957–27 © 2005 Taylor & Francis Group Ltd DOI: 10.1080/09500690500068626
958 M. Zion et al. facilitates the development of scientific literacy by enhancing students’ understanding of the nature of scientific inquiry (Chinn & Malhotra, 2002; Gallagher, 1991; Lemke, 1990; NRC, 1996; Schmidt, 1967; Solomon, 1991, 2000; Tobin & Gallagher, 1987). Junior high school and high school students are able to direct their own investigative activity, and complete all stages of scientific investigation, such as: studying complex phenomena by formulating hypotheses, controlling variables, designing experiments, collecting and analyzing information, and drawing conclusions (Tamir et al., 1982; Gott et al., 1999). These activities foster students’ natural curiosity, promote scientific activity as an intellectual value, and reinforce the view that the world is subject to investigation. Furthermore, opportunities to experience sciencein-the-making together with the ability to engage in discourse may lead to a better understanding of the nature of scientific research (Bell et al., 2003). Inquiry learning reflects the constructivist paradigm of learning. The important epistemological assumption of constructivism is that knowledge is a function of how the individual creates meaning from his/her experiences. Constructivist educators strive to create environments where learners ‘are required to examine thinking and learning processes, collect, record, and analyze data; formulate and test hypotheses; reflects on previous understandings; and construct their own meaning’ (Crotty, 1994: 31). Kuhn (2002) described constructivist scientific activity as involving the following three stages: inquiry, analysis, and inference. During the inquiry stage, investigators formulate questions that can be tested experimentally. In the analysis stage, investigators design and conduct informative experiments. Finally, in the inference stage, investigators modify their theories on the basis of evidence that emerged from their experiments. Nonetheless, although the educational potential for inquiry learning is remarkable, this learning cannot be achieved merely by placing students in the midst of a complex scientific domain for free-reign investigation (Germann et al., 1996). Findings from research on scientific thinking suggest that adolescents, and even lay adults, frequently experience difficulties in all stages of the scientific inquiry process. First and foremost, students face considerable difficulties in formulating questions (Rop, 2003). In formulating hypotheses, students frequently fail to specify variables of interest and the relationship among these variables (Njoo & De Jong, 1993). In addition, students tend to avoid precise hypotheses, which have a high risk of being rejected (Klayman & Ha, 1987; Klahr et al., 1993). In the experimentation stage, students often design inconclusive experiments, or experiments that do not test their hypotheses (De Jong & Van Joolingen, 1998). Finally, when it comes to interpreting experimental evidence, students tend to ignore, reject, or misinterpret data that do not fit into their existing theories (Chinn & Brewer, 1993; Kuhn et al., 1995). Moreover, students often develop an objectivist orientation towards science, viewing the process of science as seeking facts rather than as the construction of knowledge (Tobin et al., 1995). Penner and Klahr (1996) found that students failed to perceive the essence of inquiry as a process that combines an experimental and an intellectual aspect, a process whose aim is to suggest and examine explanations for natural phenomena. Students often do not see science as scientists’ efforts to construct
Metacognitive Instruction with Asynchronous Learning 959 explanations for phenomena in the ‘real world’, and students often do not understand that knowledge is the product of a never-ending process, continually re-examined and updated. Students work according to the ‘engineering model’, instead of a ‘scientific model’ (Schauble et al., 1991). In other words, students see scientific assignments as an experience aimed at anticipated results, and plan their experiments accordingly. Faced with all these difficulties, psychological and educational researchers presume that students’ scientific inquiry skills are not static and these skills depend on a variety of cognitive and contextual factors. These factors include interest and motivation in science, epistemological understanding of the scientific process and its value (Smith et al., 2000), familiarity with the domain of investigation, and the context of the activity (Germann et al., 1996; Kuhn et al., 1995), environmental support of inquiry activities (Greeno, 2001), and communication abilities (Germann et al., 1996). These studies alert science education researchers not to view students’ performance on inquiry tasks solely as a function of their scientific and experimental skills. In order to benefit fully from inquiry activities, both epistemic demand and regulation of cognition appeared to be crucial components in all stages of students’ investigative efforts (Bell et al., 2003). Epistemic demand can focus the student on the task and can improve the outcome of the inquiry learning activities. In order to facilitate the activity of epistemic demand, the student may be guided in small steps to the execution of a certain inquiry stage. For example, guidance assisting the hypothesis stage may supply the student with a model for the structure of a hypothetical sentence. These instructions provide students with general and cognitive strategies that may be used to perform their learning tasks (Hong et al., 2001). However, epistemic demand alone may not be enough to change students’ view of inquiry (Bell et al., 2003). Students need to use regulation of cognition to monitor the solution (Hong et al., 2001; Kluwe & Freidricksen, 1985). Dewey was correct when he said that ‘we learn by doing and by thinking about what we are doing’ (Rowe, 1978: 216). The regulation of cognition and not the knowledge of cognition is a predictor in open-ended problem-solving (Hong et al., 2001). The following section discusses how a support that targets the development of regulation of cognition (metalevel cognition) contributes to the inquiry learning process. Inquiry learning and metacognitive activity Metacognitive skills development is typically fostered by asking students to reflect on and explicitly monitor their learning performance (Lin & Lehman, 1999). The idea behind this learning ‘how to learn’ approach is helping students develop, monitor, and revise their own investigative strategies. Regulation of cognition emphasizes that students need to be reflective inquirers to successfully complete their projects, gain a deeper understanding of the domain content and the scientific process, and improve their science skills (Loh et al., 2001). Loh et al. (2001) characterize reflective inquiry according to three sets of activities. The first set involves creating a carefully organized and managed record of progress. The second set involves a reflective inquirer
960 M. Zion et al. who needs to monitor the progress in the investigation, reflecting on it, and revising investigation strategies to improve progress toward the research goal. The third set involves how a reflective inquirer should act within a learning community. Social constructivists advocate that, through discourse and conversations within a learning community that is situated in meaningful contexts, students construct new knowledge. This new knowledge is constructed by communicating one’s process and results to others, and by negotiating their ideas (Collins et al., 1989). To support the regulation of cognition and to improve the interaction between learners, students should be instructed and trained, and their learning process should be supported by metacognitive guidance (King, 1990; Kramarski et al., 2002; Meloth & Deering, 1992; Mevarech & Kramarski, 1997). Metacognitive guidance assists inquiry learning in several ways. First, this guidance helps to centre the inquiry approach on the scientific process, and not on scientific facts. Second, it helps students to be mindful of the investigation goals (Shute & Glaser, 1990; Schauble et al., 1991). Finally, metacognitive guidance assists students in reflecting on each step of their inquiry activities. Although current research has shown the positive effect of metacognitive instruction on achievements (Keselman, 2003), there is a reason to suppose that students would be unable to activate metacognitive processes in complex content such as scientific inquiry. Students often have difficulty in remembering what they did during the inquiry process, and they also fail to document their thinking. This problem may be compounded in an asynchronous learning networked (ALN) environment. Students’ achievements and ALN environment ALN is defined as a virtual classroom involving asynchronous interaction and the exchange of information exclusively on line (Hiltz, 1994; Lin & Lehman, 1999). In an ALN environment, learners can take their time to think (Benbaum-Fich & Hiltz, 2002), and they can interact with each other without geographical constraints (Downes, 1998) or personality prejudice (Rheingold, 1993). Furthermore, Cohen and Goodlad (1994) and Scardamalia and Bereitner (1994) found that an ALN learning environment helped reduce the number of passive students. An ALN learning environment enables peer communication and digital documentation of the learning process. Using ALN digital documentation, students can monitor their progress, review their learning, and reflect and revise their strategies. Indeed, both the act of communicating and the act of creating a record of the discussion can serve as a catalyst for metacognitive inquiry activities. Of 15 published empirical studies that compared face to face with online classes, almost 55% found ‘no significant difference’ in the outcomes measured, while approximately 45% reported that ALN is superior in some way to face-to-face (F2F) instruction (Alavi et al., 1997; Spencer & Hiltz, 2001). Why, then, are the potential benefits of ALN discussion groups not always realized? This phenomenon may result from a lack of metacognitive guidance, an element that improves inquiry learning and oral behavior (Glasson & Lalik, 1993; Gott et al., 1999; King, 1990, 1994; Meloth & Deering, 1992, 1994; Savery & Duffy, 1995). Given these findings, we designed an
Metacognitive Instruction with Asynchronous Learning 961
The student
Performs and improves MINT learning environment Cognitive dimension
Metacognitive dimension
Social dimension
Technological dimension
Content dependent inquiry learning
Metacognitive guidance at the content and the skill levels
Discourse in discussion group
Asynchronous discourse in discussion group
Developing inquiry skills
Developing metacognitive skills
Figure 1.
Improving discourse quality and knowledge sharing
Recording the learning process
The MINT model.
innovative learning environment, called metacognitive-guided inquiry within networked technology (MINT) (Figure 1). Figure 1. The MINT model.
Metacognitive-guided inquiry within networked technology MINT is a digital inquiry-based learning environment in which students from different schools receive metacognitive guidance during the process of performing inquiry tasks. The MINT learning environment model (Figure 1) is constructed upon four dimensions, and has the potential to meet the criteria for the design of an inquirybased learning environment (Brown et al., 1989; Valanides, 2003): 1. Cognitive dimension: MINT is a learner-centered environment, in which students experience a cognitive process of inquiry. Domain-specific design specifications are considered important elements to increase learning outcomes in problem-solving (Doornekamp, 2001). Thus, MINT is also knowledge-centered, and engages students with thought-provoking phenomena based on science curriculum. 2. Metacognitive dimension: in MINT, students obtain opportunities to improve their thinking, by using explicit metacognitive guidance and the ALN technological facilities.
962 M. Zion et al. 3. Social dimension: MINT is community-centered. MINT students belong to an ALN discussion group, in which students cooperate with one another, negotiate differences of opinion, and share various ideas and ways of thinking during an inquiry learning process. 4. Technological dimension: in MINT, all the digital written discourses are coded, and may be used by students to reflect upon and monitor their learning process. Students can regulate their learning process autonomously or with the help of peer discussion. The purpose of the present study is to examine the effects of the two main components of the MINT model on the development of inquiry skills: ALN environment, and metacognitive guidance. Using a 2 x 2 (ALN versus F2F x metacognitive guidance: provided or not provided) approach will enable us to examine the added value of each component on the development of general scientific ability and domainspecific inquiry skills. In particular, this study addresses the following issues: 1. What is the role of metacognitive instruction within the F2F environment? 2. What is the role of metacognitive instruction within the ALN environment? 3. What is the added value of metacognitive instruction when it is embedded either within the F2F or ALN environment? To the best our knowledge, these issues have not been addressed before. Method Participants Participants were 407 10th-grade students (198 boys and 209 girls) who studied in 16 classes, randomly selected from five Israeli high schools. The average student’s age was 16.3 years. The five schools were randomly selected from a pool of 15 schools that have the requisite technology infrastructure: computers and connection to the internet. The schools were similar in the following parameters: size, average socioeconomic status (as defined by the Israel Ministry of Education), mean age of students, and levels of science achievement, as assessed before the beginning of the study (see later). Within schools, classes were normally divided in terms of students’ ability and prior knowledge. Sixteen teachers (four male and 12 female) participated in the study. All the teachers had more than six years of experience in teaching biology, and all had taught in heterogeneous classrooms. Furthermore, all teachers had a degree in biology and biology education. The teachers were exposed to a two-day inservice training program (as described later). The ‘Invitation to Inquiry’: the learning unit The ‘Invitation to Inquiry’ is the learning unit used in the present study. This unit’s main goal is to develop students’ scientific knowledge and inquiry skills. To accomplish this goal, the ‘Invitation to Inquiry’ recommends experiential inquiry learning
Metacognitive Instruction with Asynchronous Learning 963 in place of learning about inquiry (Schwab, 1963). Based on this approach, four ‘Invitation to Inquiry’ items were specially designed for the present study. Each ‘Invitation to Inquiry’ represents a significant scientific event in the history of microbiology. The field of microbiology was selected for three reasons. First, according to the Israeli national biology curriculum (Israel Ministry of Education, 1991) microbiology is taught in the 10th grade. Second, microbiology has been recognized as an important subject for the development of a basic scientific knowledge (NRC, 1996). Finally, microbiology exposes students to scientific knowledge that is relevant to their everyday life, such as antibiotics, vaccinations, and hygiene. Each ‘Invitation to Inquiry’ learning session is composed of three parts, and exposed students to different types of inquiry. The first part focuses on guided inquiry, and asks students to explain a biological phenomenon. Students are required to identify a scientific problem, analyzing relevant scientific data, formulate hypotheses, plan experiments to test the hypotheses, and explain the biological basis for the experiments. In the second part, students engaged in structured inquiry. Here, students read the results of a practical experiment relating to the biological phenomenon described in the first part. The students are required to identify the experiment’s components: dependent and independent variables, measuring techniques, control, and repetition. The students also described results of experiments and reached valid conclusions based on data and results. The third part engaged students in the beginning of an open inquiry activity. This activity required students to design a follow-up experiment based on both the information cited in the second part, and on new biological information presented in the third part. Appendix 1 contains an example of a microbiology ‘Invitation to Inquiry’ learning unit. Learning groups Four types of learning groups were examined in the present study: ALN embedded within metacognitive guidance (ALN + META group), F2F with metacognitive guidance (F2F + META group), ALN with no metacognitive guidance (ALN group), and F2F with no metacognitive guidance (F2F group). All students studied four units of ‘Invitation to Inquiry’ for 12 weeks. Students used exactly the same textbook and learning materials. The differences between these four research groups were in the instructional method to which students were exposed. We will first describe the main components that served as a basis for the establishment of the four instructional methods, and then describe how these components are combined. Metacognitive guidance As referenced in Appendix 2, the metacognitive guidance used in this study was based on the techniques suggested by Mevarech and Kramarski (1997). There were no differences between the metacognitive guidance that was introduced via the ALN
964 M. Zion et al. and F2F approaches. The metacognitive guidance used two sets of metacognitive questions for regulating the learning processes: metacognitive consciousness, and executive questions. Metacognitive consciousness questions refer to knowledge about problem-solvers, about the goals of the assignment, and about problemsolving strategies. A question ‘about the problem-solver’ guides the students to describe the benefits they obtained from the learning group work and how it helped them to advance their inquiry solution. A question ‘about the assignment’ guides the problem-solvers to describe the goals of the tasks. Finally, the students were asked to describe the strategies they implemented to solve the problem and the reasons for choosing those strategies. The executive questions train students in regulating, controlling, and criticizing the cognitive processes and products. Corollary, executive questions include planning, monitoring, and evaluation questions. The planning questions guide the problem-solvers to describe their thoughts before they began solving the inquiry problem, and how they decided on the order of activities by which they used to solve the problem. The monitoring questions guide the solvers to describe when and how they assessed their activity throughout their solution process, which difficulties they encountered, and whether they obtained the most out of the solution. Evaluation question guide the problem-solvers to describe how and in what ways they improved their functioning during the inquiry problem-solving process. Both sets of metacognitive questions—metacognitive consciousness and executive questions—prompt students to reflect on their learning process. Students were told that asking and answering the metacognitive questions would help them to understand and remember the inquiry process. The teacher introduced the use of the metacognitive question-answering technique, and demonstrated how these questions may be used, while the first ‘Invitation to Inquiry’ was presented to the entire class. The metacognitive guidance was further introduced to the students upon completion of each one of the other three ‘Invitation to Inquiry’ learning sessions. Each student answered the metacognitive questions autonomously by recording his/ her answers into his/her journal. Discussion groups: F2F or ALN The learning process was based also on the instructional method developed by Mevarech and Kramarski (1997). The learning process was divided into the following three stages: In the first stage, a teacher presented one of the ‘Invitation to Inquiry’ sessions to the entire class. Teachers demonstrated inquiry learning strategies in a traditional expository manner. Afterwards, material pertaining to the other three ‘Invitation to Inquiry’ sessions will be solved later in the students’ discussion groups. In the second stage, students participated in cooperative workshops within discussion groups. These workshops were based on three, three-week microbiology ‘Invitation to Inquiry’ sessions. Students learned in heterogeneous teams of four students:
Metacognitive Instruction with Asynchronous Learning 965 each team included one high-achievement student, two middle-achievement students, and one low-achievement student. To ensure team heterogeneity, all students took a ninth-grade biology test before the beginning of the study. Teams were formed on the basis of students’ scores on this biology test. The discussion groups’ technique followed the method suggested by Brown and Palincsar (1989), and was implemented as follows. Each student read the ‘Invitation to Inquiry’ and participated in the problem-solving processes. Whenever there was no consensus, the team discussed the issue until the disagreements were resolved. In team discussions, students explained the problem to each other, approached the problem from different perspectives, balanced the perspectives against each other, and proceeded according to what seemed to be the best option at the time. When all team members agreed on a solution, they wrote it down on their answer sheets. When none of the team members knew how to complete a task, they asked for the teacher’s assistance. At the end of the period, the teacher reviewed the main ideas of the scientific inquiry processes with the entire class. At the conclusion of each ‘Invitation to Inquiry’ session, the teachers referred to the new biological knowledge attained by the students’ groups and corrected scientific errors that arose during student discussions.
Combining the learning components into four research conditions We designed two sets of learning materials for the purpose of the present study. One set of ‘Invitations to Inquiry’ included the metacognitive guidance, and the other set did not. Except for the metacognitive guidance, the two sets of learning materials were identical. These two sets of learning materials were embedded within the four research groups as follows: ●
●
●
ALN + META research group. Five to four students, each one from a different school, studied in small heterogeneous learning groups. Students did not know each other prior to joining their learning group. Students completed a session of ‘Invitation to Inquiry’ every three weeks, and then were requested to respond in writing to a metacognitive guidance. Students in the ALN + META research group could use written discourse that was coded by the computers. Each student was personally directed to the discourse recorded online in order to answer the metacognitive questions in writing. F2F + META research group. Students from the same classroom/class studied in small heterogeneous learning groups of four to five students. This research group received metacognitive guidance similar to that of the ALN + META group, except that here students had to remember their problem-solving activities, while activating the metacognitive processes. ALN research group. Five students, each from a different school, studied in small heterogeneous learning groups. Students did not know each other prior to joining their learning group. In this ALN research group, students were not exposed to metacognitive guidance.
966 M. Zion et al. ●
F2F research group. Students from the same class/classroom studied in small heterogeneous learning groups of four to five students. These students were not exposed to metacognitive guidance.
Teacher training Prior to the beginning of the study, all 16 teachers were exposed to a two-day inservice training session that focused on pedagogical issues related to inquiry teaching. Teachers were told that they were participating in an experiment in which new learning materials were used. Teachers were divided into groups according to the four learning groups. In each training group, teachers were exposed separately to the scientific and educational theoretical background of their learning method and its practical implications. The scientific knowledge that is required of the teachers is identical in all training groups. The teacher training introduced microbiology concepts relevant to the ‘Invitations to Inquiry’. In addition, the teachers were encouraged to use instructions such as ‘discuss your science ideas with your team’, or ‘explain your answers’. In particular, the teachers who were assigned to the metacognitive research groups (ALN + META and F2F + META) were introduced to the rationale and techniques of the metacognitive guidance method. However, all teachers were introduced to the rationale of cooperative learning, and how to implement cooperative learning in each type of learning environment. Measurements We used two sets of measurements to examine the effects of each research group on students’ general scientific ability and inquiry skills: The Test of General Scientific Ability (TGSA), and The Test of Domain-specific Inquiry Skills (TODIS). The Test of General Scientific Ability. The TGSA was designed by the authors on the basis of the Fraser T.O.E.S. test (Fraser, 1980). The TGSA includes 15 independent multiple choice items assessing general scientific ability. Following Fraser (1980), the TGSA focuses on the following scientific skills: measuring, constructing graphs and interpreting, comprehending information, designing experiments, and drawing conclusions. Each item was scored as either 1 (correct) or 0 (incorrect), and a total score ranged from 0 to 15. Alpha Cronbach reliability scores are 0.75 and 0.78 on the pretest and post-test, respectively. The correlation between the pretest and post-test scores was 0.56. Test of Domain-Specific Inquiry Skills. The TODIS is a domain-specific examination designed by the authors, similar to the scientific examination suggested by The Programme for International Student Assessment (PISA) (Organization for Economic Co-operation and Development, 1999, 2003). TODIS is constructed in two parts. The first part (10 open-ended items) describes a significant scientific
Metacognitive Instruction with Asynchronous Learning 967 event in the field of microbiology. Students were presented with the phenomenon, the findings collected by scientists that described the phenomenon, and the experiments designed by scientists for solving the problem. Students were required to identify the relevant variables, interpret the results of the given experiment, and draw valid conclusions on the basis of the given data. In the second part (five openended items), students were requested to formulate additional hypotheses on the basis of the data collected, design (in writing) a follow-up experiment, identify the relevant variables, and explain the rationale for the suggested experiment. The TODIS has two versions: one was used as a pretest and the other as a posttest. The TODIS pretest describes research conducted by Watanaba (1963) on dysentery disease caused by the bacterium Shigella. Watanaba (1963) tried to understand the high rate of death among Japanese patients, after the physician prescribed four different kind antibiotics. The TODIS post-test, adopted from Gaughran (1969), refers to a middle-ages archive document from the year 1368 describing a unique phenomenon: red spots appearing on corn pudding. The farmers interpreted the red spots as Jesus’ blood, whereas the scientists tried to provide a scientific explanation for the phenomenon. Each item was scored as either 1 (correct) or 0 (incorrect), and a total score ranged from 0 to 15. Alpha Cronbach reliability scores were 0.82 and 0.79 for the pretest and post-test, respectively. The correlation between the pretest and post-test total scores was 0.54. Factor analysis of the pretest indicates three factors: designing experiments, describing results and drawing conclusions, and identifying inquiry components. The first, second, and third factors account for 62%, 23%, and 15% of the variance, respectively. Procedure One month after the beginning of the school year all students were administered the TGSA, and one week later students took the pretest version of the TODIS. We assumed that students would gain confidence in taking the TODIS after undertaking the non-domain-specific test (TGSA). Teachers began teaching according to the instructional method to which they were assigned, using the materials specially designed for their group. To ensure that the instruction was properly implemented as designed, all classrooms were observed twice a week by one of the authors of this article for three months. At the end of the three months, all students were readministered the TGSA and TODIS (post-test versions). Results The present study examined the effects of four research groups (ALN + META, F2F + META, ALN, and F2F) on students’ general scientific ability and domainspecific inquiry skills. Because significant correlations were found between TGSA and TODIS scores on the pretest and post-test (r = 0.47 and r = 0.57, respectively; both p < 0.0001), we performed multiple analysis of variance (MANOVA) on the
968 M. Zion et al. pretest and multiple analysis of covariance (MANCOVA) (Wilks’s lambda test) on the post-test scores, with pretest scores used as a covariance (Huck, 2004: 412–413). Before performing the MANCOVA, we checked and obtained the prerequisites for running this test (MSe = 2.1, F(6, 803) < 1.00, p > 0.05). The MANOVA and MANCOVA were followed by univariance and unicovariance analyses, respectively. The results indicate that before the study began no significant differences were found on TGSA and TODIS simultaneously (F(6, 804) = 1.16; p > 0.32). However, at the end of the study significant differences between research groups were found on the TGSA and TODIS post-test scores simultaneously, controlling for both pretest scores (MSe = 11.2, F(6, 800) = 45.07, p < 0.0001). Given these findings, we report in the following on the univariate analyses of TGSA and TODIS scores by time and research groups. General scientific ability Table 1 presents the mean scores and standard deviations (SDs) of general scientific inquiry ability by time (pretests and post-tests), and research groups. Table 2 presents the mean scores and SDs of each TGSA component by time and research groups. In addition, Tables 1 and 2 present the adjusted post-tests mean scores to test for differences between research groups, with post-test scores as the dependent variable and pretest scores as the covariate. According to Table 1, before the study began, no significant differences were found between research groups, neither on the total score (MS = 5.03; F(3, 403) = 1.89; p > 0.12), nor on any of its components (F(3, 403) = 1.02–2.59; all p > 0.05). Upon completion of the study, however, significant differences between research groups were found on the TGSA post-test total score (MS = 1.54; F(3, 402) = 54.08; p < 0.0001). Post-hoc analyses of the adjusted mean scores based on the pairwise comparison t-test technique indicates that the ALN + META group significantly outperformed the ALN and F2F + META research groups. These two research groups in turn significantly outperformed the F2F group (p < 0.05). No significant differences were found, however, on the total post-test scores between the ALN and F2F + META research groups (p > 0.05). Table 1.
Students’ mean scores, adjusted mean scores and SDs on general scientific ability (TGSA), by time and research group
Pretest mean SD Post-test mean Adjusted mean SD
ALN + META (n = 102)
F2F + META (n = 100)
ALN (n = 97)
F2F (n = 108)
6.97 2.19 11.51 11.71 1.95
7.23 2.21 10.49 10.43 2.25
6.98 2.06 10.55 10.62 2.03
7.27 2.21 9.09 9.01 2.50
Note: Scores ranged from 0 to 15.
Metacognitive Instruction with Asynchronous Learning 969
Table 2. Students’ mean scores, adjusted mean scores and SDs on general scientific ability components (TGSA), by time and research group ALN + META (n = 102)
F2F + META (n = 100)
ALN (n = 97)
F2F (n = 108)
1.86 1.11
1.79 0.92
1.74 1.09
1.84 0.84
1.99 1.99 1.06
1.88 1.90 0.85
1.89 2.01 1.07
1.86 1.85 0.79
2.01 0.89
2.02 0.94
1.87 0.68
2.05 2.03 0.88
2.09 2.03 0.89
1.93 1.99 0.74
1.51 0.77
1.34 0.81
1.49 0.67
2.87 2.84 0.32
2.86 2.89 0.47
2.42 2.40 0.53
0.21 0.45
0.22 0.39
0.28 0.40
0.22 0.38
1.75 1.85 0.98
0.89 0.90 0.82
0.91 0.86 0.99
0.54 0.56 0.80
1.60 0.78
1.70 0.74
1.59 0.75
1.85 0.80
2.88 2.89 0.37
2.79 2.76 0.57
2.81 2.84 0.56
2.32 2.20 0.66
Measuring Pretest Mean SD Post-test Mean Adjusted mean SD
Constructing and interpreting graphs Pretest Mean 1.93 SD 1.03 Post-test Mean 2.04 Adjusted mean 2.09 SD 0.95 Comprehending information Pretest Mean 1.37 SD 0.79 Post-test Mean 2.84 Adjusted mean 2.88 SD 0.45 Designing experiments Pretest Mean SD Post-test Mean Adjusted mean SD Drawing conclusions Pretest Mean SD Post-test Mean Adjusted mean SD
Note: Scores ranged from 0 to 3.
970 M. Zion et al. Further analyses of each post-test component of the TGSA indicate significant differences between research groups on three components: comprehending information (F(3, 402) = 7.21; p < 0.0001), designing experiments (F(3, 402) = 41.53; p < 0.0001), and drawing conclusions on the basis of scientific data (F(3, 402) = 14.13, p < 0.0001). The differences between the research groups on measuring, constructing and interpreting graphs post-test scores were not significant (F(3, 402) values = 1.38 and 0.74, respectively; both p > 0.05). Given the findings of the TGSA total scores, we further analyzed the effects of the four learning environments on each component of the TGSA: comprehending information, designing experiments, and drawing conclusions on the basis of scientific data. The following findings were obtained. On designing experiments, the mean score of the ALN + META research group indicates that this group significantly outperformed the ALN and F2F + META research groups, and the ALN and F2F + META research groups significantly outperformed the F2F research group (p = 0.036–0.0001). Regarding the TGSA components, comprehending information and drawing conclusions, the F2F group scored significantly lower than other research groups (p = 0.024–0.0001), but no significant differences were found between other research groups (p = 0.24–0.52). A summary of students’ performances, by dependent variable and research group, appears in Table 3. Domain-specific inquiry skills Table 4 presents the mean scores and SDs of students’ achievements on the domainspecific inquiry skills test by time (pretests and post-tests), and research groups. Table 5 presents the mean scores and SDs of each component of TODIS by time and research group. In addition, Tables 4 and 5 present the adjusted post-test mean
Table 3.
Summary of students’ performances by dependent variable and research group
Dependent variable General scientific ability—TGSA total scores Measuring Constructing and interpreting graphs Comprehending information Designing experiments Drawing conclusions Domain-specific inquiry skills—TODIS total scores Identifying inquiry components Describing results and drawing conclusions Designing experiments
Finding a>b=c>d a=b=c=d a=b=c=d a=b=c>d a>b=c>d a=b=c>d a>b=c>d a=b=c>d a>b=c>d a>b>c=d
Note: a, ALN + META; b, F2F + META; c, ALN; d, F2F. =, there are no significant differences between the two research groups; >, significant difference in the mean scores of the two research groups.
Metacognitive Instruction with Asynchronous Learning 971 Table 4.
Students’ mean scores, adjusted mean scores and SDs on domain-specific (microbiology) inquiry skills, by time and research group ALN + META (n = 102)
F2F + META (n = 100)
ALN (n = 97)
F2F (n = 108)
5.11 1.76 9.39 9.88 1.48
5.25 1.90 8.62 8.69 1.69
5.41 1.87 8.29 8.17 1.95
5.27 1.70 6.83 6.80 2.18
Pretest mean SD Post-test mean Adjusted mean SD
Note: Scores ranged from 0 to 15.
Table 5.
Students’ mean scores, adjusted mean scores and SDs on domain-specific (microbiology) inquiry skills, by time and research group ALN + META (n = 102)
Identifying inquiry components Pretest Mean 2.36 SD 0.72 Post-test Mean 3.88 Adjusted mean 4.00 SD 0.67
F2F + META (n = 100)
ALN (n = 97)
F2F (n = 108)
2.49 0.70
2.55 0.70
2.46 0.71
3.85 3.84 0.60
3.87 3.84 0.69
3.20 3.22 0.73
2.02 1.40
1.97 1.22
2.84 2.79 1.13
2.34 2.29 1.28
Describing results and drawing conclusions Pretest Mean 1.87 1.87 SD 1.66 1.58 Post-test Mean 3.35 2.87 Adjusted mean 3.37 2.93 SD 0.97 1.10 Designing experiments Pretest Mean SD Post-test Mean Adjusted mean SD
0.88 0.54
0.89 0.53
0.84 0.61
0.84 0.40
2.54 2.51 0.89
1.95 1.92 0.89
1.48 1.54 0.63
1.29 1.29 1.11
Note: Scores ranged from 0 to 5.
972 M. Zion et al. scores to test for differences between research groups, with post-test scores as the dependent variable and pretest scores as the covariate. Analysis of variance indicates no significant differences between research groups before the study began, neither on the total scores (Mse = 3.28, F(3, 403) = 0.43, p > 0.05) nor on any pretest component (F(3, 403) = 0.33–1.28, all p > 0.05). Upon completion of the study, however, significant differences between research groups were found on the total post-test scores (Mse = 1.94, F(3, 402) = 5.09, p < 0.05) and on each of the three post-test components: Identifying inquiry components, describing results and drawing conclusions, and designing experiments (F(3, 402) = 11.42, 29.52, and 20.50, respectively; all p < 0.0001). Post-hoc analyses of the adjusted TODIS post-test total mean scores based on the pair-wise comparison t-test technique indicate that the ALN + META research group significantly outperformed the F2F + META and ALN research groups, and these two research groups significantly outperformed the F2F research group, whereas no significant differences were found between the F2F + META and ALN groups. Post-hoc analysis of each TODIS component indicates the following. With regard to identifying inquiry components, the F2F research group obtained significantly lower scores than other research groups (p = 0.003–0.0001), but no significant differences were found between the other three research groups (p > 0.05). On describing results and drawing conclusions, the ALN + META group significantly outperformed the F2F + META and ALN research groups. The F2F +META and ALN research groups significantly outperformed the F2F research group (p = 0.01– 0.03). On designing experiments, the ALN + META research group significantly outperformed the F2F + META research group, and the F2F + META research group significantly outperformed the ALN and F2F research groups (p = 0.02– 0.01); no significant differences were found between the ALN and F2F research groups (p > 0.05). Table 3 presents a summary of students’ performances, by dependent variable and research group. Discussion The findings of the present study indicate that students who studied science in the ALN + META environment significantly outperformed students who studied in the ALN or F2F + META environments. Furthermore, these two research groups significantly outperformed their F2F counterparts on general scientific ability and inquiry skills in the domain-specific learning (microbiology). The discussion will focus on the following issues: the role of ALN versus F2F in facilitating learning; the differential roles of metacogitive guidance embedded in different kinds of environments in enhancing general and domain-specific science learning; the added value of combining ALN and metacognitive guidance compared with each of its components; the development of general and domainspecific knowledge within each research group; and suggestions for future research.
Metacognitive Instruction with Asynchronous Learning 973 The role of ALN versus F2F environments in facilitating science learning The main advantage of the ALN and F2F environments is the fact that both enable students to share ideas, resolve cognitive conflicts, and engage in mutual thinking (for example, Benbaum-Fich & Hiltz, 2002; Mevarech & Light, 1992). Yet, each environment has its unique advantages and disadvantages. One may (mistakenly) argue that the fact that ALN students outperformed the F2F students is an artifact caused by the ALN, which is a technologically new and enthusiastic mean of interaction. This hypothesis should be rejected because 10th-grade students may be used to work with the internet and to communicate via emails. In this regard, ALN is not an innovative technology. With regard to ALN, it provides ample opportunities for students from different schools, regions, races, or countries to study together without being aware of the partners’ backgrounds. Of course, the F2F environment also enables students from different schools to study together, but this is very unlikely to occur in regular classrooms during the ongoing school studies. In addition, although both ALN and F2F learning environments are cooperative learning settings (Fischer and Mandl 2000), students’ interaction within ALN environment is asynchronic and communicates in writing. In contrast, students’ interaction within the F2F environment is synchronically and communicates orally. Moreover, ALN students can also utilize the coded discourse, which is usually unfeasible for F2F students. Given the advantages of ALN over F2F, one cannot ignore the lack of oral communication in ALN interaction. This finding raises several questions. For example, do ALN students compensate for the lack of their oral communication? And if so, how do they compensate? In addition, does the metacognitive guidance improve students’ interaction within ALN? These questions are open for future research. The present study indicates, however, that ALN students outperformed the F2F students on both measures of general and domain-specific scientific performance. Probably, the written discourse, which is the main mode of interaction in the ALN environment, has stronger positive effects on students’ general and domain-specific performance, than F2F interaction, which is mainly based on oral communication.
The differential roles of metacognitive guidance in enhancing general and domain-specific science learning The findings of the present study incorporate the findings of previous studies showing the positive effects of metacognitive guidance on learning outcomes (Baker, 2002; Munby et al., 2002). However, previous studies compared the effects of instructional methods (with or without implementing metacognitive) guidance on learning outcomes (Baker, 2002; Munby et al., 2002); in the present study, metacognitive guidance was embedded in different kinds of learning environments. One environment utilized high tech (ALN) and the other environment did not (F2F). Furthermore, to our knowledge, no previous study focused on the roles of metacognitive guidance in enhancing general and domain-specific knowledge simultaneously.
974 M. Zion et al. The findings show that metacognitive guidance did not act in the same way on enhancing general scientific ability compared with domain-specific inquiry skills. In order to succeed in a test that is similar in its domain-specificity to the learning task that students have already performed, a ‘close transfer’ learning process should occur. In contrast, students’ improvement in general scientific ability after having a domain-specific inquiry learning process is considered as a ‘far transfer’ of knowledge and skills. We found that the effects on domain-specific inquiry skills were greater than those on general scientific ability. This finding is expected, given the large number of studies showing that ‘far transfer’ of knowledge and skills is more difficult to attain than ‘close transfer’ (Zohar, 2004: 184). This phenomenon may also explain why no significant differences between research groups were found on ‘measuring’ and ‘constructing and interpreting graphs’, two topics that were not part of the learning unit. For the same reason we can explain the strong effects of metacognition on designing experiments, which is the main topic of the ‘Invitation to Inquiry’, the learning unit of the present study. The benefits of ALN embedded within metacognitive guidance for enhancing general scientific ability and inquiry skills Research on metacognitive guidance suggests several ways for improving achievements: through social collaboration (Mevarech & Kramarski, 1997), reflection on various stages (Lin & Lehman, 1999), evaluation of peer work (Kuhn, 2002), and online support (Salmon, 2002). The present study enlarges these findings by showing the strong positive effects of ALN + META on general scientific ability and domain-specific inquiry skills. The findings of the present study further show that combining ALN and metacognitive guidance is more efficient than implementing each element by itself for enhancing general scientific ability and domain-specific inquiry skills. In the absence of ALN and metacognitive guidance, students’ scientific performances were rather poor. Young (1994) claims that the ALN written discourse requires students to create and clarify connections between concepts. The present study indicates that the metacognitive guidance facilitates these processes. Another interesting finding relates to the fact that no significant differences were found between the ALN and F2F + META research groups. Perhaps because ALN students must write their responses, this might lead these students to control and reflect on the tasks as did the students who were exposed to metacognitive guidance within the F2F environment. This is not the case, however, for the F2F students who were not exposed to metacognitive guidance, and thus did not activate metacognitive processes regularly. The findings that relate to the knowledge and skills that emerged from the domain-specific test show that the ALN + META research group significantly outperformed the ALN and F2F + META research groups; and these two groups significantly outperformed the F2F group. The differences in performance were particularly evident in designing an experiment and describing results and drawing conclusions. This finding should not surprise us, because the main topics of the
Metacognitive Instruction with Asynchronous Learning 975 ‘Invitation to Inquiry’ unit deals with formulating hypotheses and testing them, the ability to design an experiment, and describe and analyze the results (Schwab, 1963). Shin et al. (2003: 27) indicate that ‘Regulation of cognition, including planning and monitoring skills, was a strong predictor of ill-structured problem solving in unfamiliar contexts’. We suggest, therefore, that recording the learning process via ALN on the one hand, and the metacognitive activities (self-controlling, reflecting, and decision-making) on the other hand, synergistically contribute to the planning and conclusion stages, as part of a flexible inquiry problem-solving process (Chinn & Malhotra, 2002; NRC, 2000; Shin et al., 2003). Students transferred their planning and analyzing knowledge from a task that is domain-specific and process-dependent, to a task that is neither domain-specific nor process-dependent. This is probably because of the large overlap between the two tasks (Salomon & Perkins, 1989). Despite this claim, and based on the fact that problem-solving requires cognitive activities and strategies that need to be combined with domain knowledge (Ennis, 1989; Taconis et al., 2001; Zohar & Tamir, 1993), we believe it will be worthwhile to examine the extent to which inquiry skills will be transferred to another domain-specific task. The present research has not examined this transfer ability. Suggestions for further research The climax of student activity in this research was a theoretical planning of an open inquiry. Shin et al. (2003) believe that students are required to judge evidence and regulate cognition only when problems are sufficiently complicated and unpredictable to challenge them. Hands-on inquiry activities add to the complexity and to the unpredictable aspect of the inquiry process (Llewellyn, 2002). We suggest, therefore, that the MINT model should be applied also in a hands-on inquiry process, in which students conduct open, practical inquiry. The MINT model includes cognitive and metacognitive components. The model does not refer to non-cognitive variables, such as motivation or attitudes towards learning. Furthermore, the model in its present form does not refer to different learning styles or to extrovert/introvert characteristics. There is a reason to suppose that students having different characteristics would benefit differently under the four research groups. This issue merits future research. Since argumentation and justification would predict success at problem-solving processes (Cho & Jonassen, 2002; Hong et al., 2001), we also suggest adding to the MINT model argumentation and justification processes that allow students to defend their solutions when other alternatives are presented. Emphasizing the dynamic characteristics of the open inquiry process may assist in the judgment and justification processes (Zion et al., 2004). But we have to take into consideration that the process of open dynamic inquiry is time-consuming: time is needed to think and for inquiry processes to transpire (Zion et al., 2004). The findings of the current research indicate that a continuous record of the inquiry process may facilitate the activation of metacognitive processes, which may reduce the time needed to perform
976 M. Zion et al. the inquiry tasks successfully. Further research may address this important issue by designing an appropriate study for this purpose. One weakness of this research is that students in the F2F discussion groups studied in the same classroom. On the other hand, the ALN discussion groups were comprised of students from three different classrooms at three different schools. Hiltz and Wellman (1997) have found that the level of innovation, information import, and discourse quality in networked discussion groups depends on the cultural gap of participants. Newman et al. (1995) indicated that the innovation level is higher in a F2F multi-participant discourse compared with networked discussion groups, when both are comprised of students from different geographical backgrounds who did not know each other. Moreover, Benbunan-Fich and Hiltz (2002) suggested that a combination of F2F contact with asynchronous interaction is potentially beneficial for students. Direct interaction occurring in F2F contact apparently improves learning in the discussion groups. For this reason, we suggest the feasibility of combining F2F contact with asynchronous interaction in the MINT model. The ‘Invitation to Inquiry’ unit is in line with the PISA conceptualization of science literacy. The findings of the present study show how ALN + META can be used as a means for enhancing science literacy. We suggest an examination of the performance of students exposed to metacognitive guidance, either within ALN or F2F settings on the PISA science examination to be administered in 2006. The promising findings of ALN + META and the challenging tasks provided in the ‘Invitation to Inquiry’ may enhance students’ science literacy, just as it enhanced students’ scientific ability and inquiry skills in the specific domain. Benbunan-Fich and Hiltz (2002: 8) have stated that ‘research should open up the black box to investigate what specific characteristics of the ALN medium and which pedagogical techniques help students to earn better grades and be more satisfied in this environment. The MINT model may provide a key to open this ‘black box’, and suggests that implicit and explicit metacognition activities are ‘the mirror on the screen’ (Salmon, 2002) that may help students succeed in performing challenging inquiry tasks. Acknowledgements The authors would like to thank Ori Stav, Yair and Racheli Feldman, and Yosef Mackler for their editorial assistance. They would also like to thank Bruria Agrest for her pedagogical support. This research was supported by Ruth and Uri Oppenheimer’s contribution in memory of Paula-Ruth and Zvi Oppenheimer. This research is also supported by the Schnitzer Foundation for Research on the Israeli Economy and Society. References Alavi, M., Yoo, Y., & Vogel, D. (1997). Using information technology (IT) to add value to management education. Academy of Management Journal, 40, 1310–1333.
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Appendix 1: the ‘Invitation to Inquiry’—an example Part A Many microorganisms exist in the intestines of rats and other healthy creatures. Pasteur (1822–1895) stated that the presence of some microbes is essential for the normal life of the organism. In recent years, researchers have found a way to breed rats that are free of microbes by using strict safeguards during the pre-natal period. Researchers raised young rats in sterilized rooms, in which they breathed sterilized air, and were fed sterilized food and water. Researchers sought to determine if the Amoebae, which causes Entamoeba histolitica, also causes this disease in microbe-free rats. Questions: A.1 Formulate an hypothesis for testing the question and explain the basis for your hypothesis. A.2 Plan an experiment that confirms your hypothesis. A.3 Describe the results which confirm your hypothesis. Explain.
Metacognitive Instruction with Asynchronous Learning 981 A.4 In your opinion, is it worthwhile to use microbe-free rats, in the experiments that you have just planned?
Part B Researchers conducted an experiment and used 30 microbe-free rats and 30 regular rats of the same species and the same age. In each of these two groups, there were an identical number of males and females. All the rats were contaminated with Amoebae and observed for several days. After this period of observation, researchers found the symptoms of the disease in 95% of the regular rats; researchers did not find any symptoms of the disease in the microbe-free rats. Questions: B.1 Identify procedural components of the experiment. B.2 Does the result of the experiment supports or refutes hypothesis which you phrased in question A.1? Discuss. B.3 Are the results of the experiment compatible with Pasteur’s statement? Discuss. B.4 What can you conclude from the results of the experiment? Discuss.
Part C I. Microbe-free rats are more vulnerable to other infectious diseases than regular rats (unlike the case of contamination with the Amoebae). Questions: C.1 Suggest an explanation for the regular rats’ resistance to infectious diseases. This resistance is greater than the resistance in the microbefree rats. C.2 According to the above information (C.1), test your conclusion in question B.4. If this information supports your conclusion, give reasons. If the information refutes your conclusion, suggest an explanation for this contradiction. C.3 Assume what that the inter-relationships exist between the microbes and the Amoebae in the regular rats’ intestines. Explain the resistance of the microbe-free rats to the Amoebae. C.4 Would you recommend administering antibiotics to the regular rats that were infected with Amoebae? Discuss. II. When regular rats are fed a vitamin deficient diet (lacking Biotin and Vitamin K), they survive for quite a long time; while within a few days, the microbe-free rats show typical symptoms of vitamin deficiency, and die within two weeks.
982 M. Zion et al. C.5 What conclusions do you draw from this information about the importance of microbes in rats intestines? Discuss. C.6 Suggest an experiment for testing the influence of microbes in the intestines of the rats. Address the following issues: (a) What is your hypothesis? What is the independent variable? (b) What is the dependent variable? (c) Which results support you hypothesis? C.7 Following the information that you have learned in part II, review the discussion you had in Question C.3. What is your opinion about this discussion in light of this new information?
Appendix 2: Metacognitive guidance Here are some questions to help you examine your learning process as you handle an inquiry problem. Metacognitive consciousness questions About the problem solver 1. Describe the benefit you obtained from your learning group work and how it helped you advance your inquiry solution. Quote some examples. About the goals of the assignment 2. What were the goals of your task? About the problem solving strategies 3. What strategies have you implemented to solve the problem, and what was the reason for choosing those strategies? Meta-guidance executive questions Planning questions 4. Describe your thoughts before you began solving the inquiry problem? Quote some examples. 5. How did you decide on the order of activities by which you came to your solution? Quote some examples. Monitoring questions 6. When and how did you assess your activity, during your solution process? Quote some examples.
Metacognitive Instruction with Asynchronous Learning 983 7. Did you encounter any difficulties in working towards the solution? Quote some examples. 8. Did you obtain the most, out of the solution of the inquiry problem? Specify? Evaluation question 9. How and in what ways did you improve your functioning during the inquiry problem solving process? Quote some examples.
