Promoting Students’ Learning in Genetics With the Learning Cycle

By Dogru-Atay, Pinar Tekkaya, Ceren

ABSTRACT. The authors investigated the comparative effect of the learning cycle and expository instruction on 8th-grade students’ achievement in genetics. They adopted the nonequivalent control group design as a type of quasiexperimental design. The experimental group (N = 104) received learning cycle instruction, and the control group (N = 109) received expository instruction. The 2-way analysis of covariance indicated a statistically significant posttreatment difference between the experimental and control groups in favor of the experimental group after instruction. Results also revealed that students’ logical thinking ability and meaningful learning orientation accounted for a significant portion of variation in genetics achievement. However, the authors found no statistically significant difference between girls’ and boys’ performance with respect to genetics achievement. Keywords: genetics achievement, learning cycle, meaningful learning orientation, reasoning ability, self-efficacy

IMPROVEMENT OF MEANINGFUL understanding of scientific concepts has long been a central goal of science education. To achieve this goal, learners must be actively engaged in meaningful learning, seek to relate new concepts to prior knowledge, and use their new conceptual understanding to explain experiences they encounter (Ausubel, 1963; Novak, 2002). According to Ausubel, meaningful learning occurs when students consciously link new knowledge to relevant concepts they already possess. Otherwise, rote learning occurs. In rote learning, students do not integrate new concepts to their prior knowledge to form a coherent framework. As a result, they tend to rely on memorizing isolated facts (Novak). Researchers claim that students who frequently use rote learning tend to generate misconceptions concerning scientific concepts (BouJaoude, 1992; Williams & Cavallo, 1995). Genetics is among such topics. Although genetics requires the ability to meaningfully relate different concepts in life science, researchers have shown that it is generally learned by rote (e.g., Stewart, 1982).

Addressing Learning Difficulties in Genetics

Genetics is considered one of the most important and difficult topics in the school science curriculum (Bahar, Johnstone, & Hansell, 1999; Finley, Stewart, & Yarroch, 1982; Lewis & Wood- Robinson, 2000; Tsui & Treagust, 2004). Researchers reported several reasons why genetics concepts are difficult for students to learn. For example, Knippels, Waarlo, and Boersma (2005) indicated that these difficulties originate mainly from the domain-specific vocabulary and terminology, the mathematical content of Mendelian genetics, the cytological processes, the complex nature of genetics, and the abstract nature of the subject matter. According to Baker and Lawson (2001), various genetics concepts depend on imaginary (theoretical) ideas constructed in abstract hypotheticodeductive conceptual systems. Therefore, sound understanding of theoretical genetics concepts requires learners to reason hypothetico- deductively. Likewise, Banet and Ayuso (2000) argued that meaningful understanding of genetics is difficult and requires a certain level of abstract thought. They also criticized the traditional teaching approach and suggested the development of more effective alternatives. Smith and Sims (1992) analyzed the research on the nature of the relation between formal operational thought and genetics problem solving. Those authors stressed the availability of instructional techniques that could facilitate comprehension of genetics. The learning cycle is one of the teaching strategies that can promote this effort (Cavallo, 1996; Lawson & Renner, 1975; Marek & Cavallo, 1997; Smith & Sims).

Research on the Learning Cycle

The learning cycle, derived from Piaget’s (1950) model of mental functioning, was introduced as part of the Science Curriculum Improvement Study to enhance elementary school students’ concept development and to enhance the introduction of formal concepts (Karplus, 1977). The learning cycle is an inquiry-based teaching strategy that divides the instruction into three phases: exploration, concept introduction, and concept application (Karplus; Purser & Renner, 1983; Renner, Abraham, & Birnie, 1988). During exploration, for example, the teacher provides learners with concrete experiences related to the content to be learned. This phase allows students to mentally examine ideas via brainstorming to identify what they already know. After exploration, the teacher introduces the concepts to the learners more explicitly. The teacher promotes a discussion period in which students share their observations with their peers. The teacher then links learner experiences to the relevant scientific concept. After the identification of scientific terminology, learners engage in additional activities in which they apply their newly developed knowledge to novel situations (Colburn & Clough, 1997; Settlage, 2000). According to Abraham and Renner (1986), there is a direct correspondence among the elements of Piaget’s mental function model and the phases of the learning cycle. For example, the exploration phase allows students to assimilate the essence of the science concept through direct experience. When students investigate a new concept through an exploration, their new experiences cause them to reevaluate their past experiences. This process produces disequilibrium, and students need to integrate the concept to reach equilibration. The concept application phase provides students with opportunities to relate the newly developed science concept to everyday applications through a cognitive process that Piaget referred to as organization. This phase helps learners to mentally organize the new experiences by forming connections with previous experiences (Marek & Cavallo, 1997; Martin, Sexton, & Gerlovich, 2001). The learning cycle makes students aware of their own reasoning by encouraging them to reflect on their previous activities. Researchers have claimed that once learners become aware of their own reasoning and apply new knowledge successfully, they become more effective in searching for new patterns (Sunal & Sunal, 2003). In summary,

the overall goal of the learning cycle is to help students construct new knowledge by creating conceptual change through interaction with the social and natural world. The inquiry teaching strategy takes into account students’ developmental levels and helps them use their prior knowledge as they learn new thought processes, develop higher levels of thinking, and became aware of their own reasoning. (Sunal & Sunal, p. 43)

Since its introduction, the learning cycle has been the focus of various studies in the field of science education. In many of these studies, researchers have documented the effectiveness of the learning cycle and demonstrated that this approach has widespread applicability to a variety of grade levels and disciplines (Abraham & Renner, 1986; Barman, Barman, & Miller, 1996; Cavallo & Laubach, 2001; Colburn & Clough, 1997; Lindgren & Bleicher, 2005; Marek & Cavallo, 1997; Odom & Kelly, 2001). Results of these studies have revealed that instruction based on the learning cycle enhanced not only conceptual understanding but also process-skill achievement. For example, Renner (1986) tested the effectiveness of the learning cycle versus expository instruction in promoting gains in content achievement and intellectual development of 9th- and 10thgrade students. Results showed that learners at the concrete level taught by the learning cycle method made significantly greater gains on concrete concepts and moved more often from one developmental level to another in comparison with students in the expository group. Purser and Renner (1983) and Schneider and Renner (1980) have reported similar findings. Working with 6th graders, Saunders and Shepardson (1987) investigated the effects of concrete (learning cycle) and formal (traditional) instruction on reasoning and science achievement. They reported statistically significant higher levels of performance in science achievement and cognitive development favoring the learning cycle instruction group and a statistically significant gender effect favoring boys. Likewise, Marek, Cowan, and Cavallo (1994) reported that learning cycle instruction was more effective than expository instruction in promoting high school students’ understanding of diffusion. In another study, Barman et al. (1996) compared the learning cycle teaching approach with a textbook or demonstration method of instruction to determine whether one method was more effective in facilitating 5th-grade students’ conceptual change concerning sound. The findings indicated that students who were taught by the learning cycle had a significantly better understanding. Balci, Cakiroglu, and Tekkaya (2006) found significant differences among the learning cycle and traditional groups in favor of the learning cycle with respect to students’ understanding of photosynthesis and respiration in plants.

Research on Cognitive and Motivational Variables

In addition to research supporting the effectiveness of the learning cycle in facilitating a better understanding of scientific concepts, several other studies have focused on identifying the variables that affect students’ achievement. Although, in some of these studies, researchers have investigated the role of cognitive variables on science achievement (BouJaoude, 1992; BouJaoude, Salloum, & Khalick, 2004; Cavallo, 1996; Johnson & Lawson, 1998; Lawson & Thompson, 1988; She, 2005), others have taken both motivational and cognitive variables into account (Cavallo, Rozman, Blickenstaff, & Walker, 2003; Cavallo, Rozman, & Potter, 2004; Kang, Scharmann, Noh, & Koh, 2005). These researchers generally used reasoning ability, learning approach, and prior knowledge as cognitive variables and self-efficacy, motivational goal, and failure tolerance as motivational variables. Much of the research, however, has focused on the cognitive aspects of teaching and learning science. Among them, reasoning ability has received the most attention from researchers. In previous studies, many researchers have consistently reported that the ability to reason formally is the strongest predictor of meaningful understanding of scientific concepts, including genetics. Lawson and Thompson mentioned that high-formal students who no longer require concrete objects to make rational judgments and are capable of hypothetical and deductive reasoning, performed better than did low-formal students. (High-formal students are able to understand both concrete and formal concepts. They have developed sound understanding of abstract concepts. Such students are capable of looking for relations, generating and testing alternative solutions to problems, and drawing conclusions by applying rules and principles. Low- formal students are concrete reasoners who are unable to develop sound understanding of abstract concepts. They are able to understand only concrete concepts. Low-formal students have not fully developed formal thought yet.) Those researchers tested the hypothesis that formal reasoning ability is essential for 7th-grade students to successfully deal with misconceptions and develop scientifically acceptable conceptions of genetics and natural selection following standard lecture-textbook-based instruction. The researchers found that the number of misconceptions is consistently, statistically, and significantly related to reasoning ability. In an earlier study, Lawson and Renner (1975) reported that interpreting and solving genetics problems requires formal-level operations such as probabilistic, combinational, and proportional reasoning that is in line with Piaget’s developmental theory. Researchers have also suggested meaningful learning orientation as a main predictor of students’ science achievement. Cavallo and Schafer (1994) defined learning orientation as the extent to which learners use meaningful or rote approaches to learning new information. Students with a meaningful learning orientation try to make connections among concepts, whereas students who do not possess a meaningful learning orientation concentrate on memorizing ideas, concepts, and facts. In their study, Cavallo and Schafer explored the relation between students’ meaningful learning orientation and their understanding of genetics topics. They found that meaningful learning orientation and prior knowledge of meiosis were significant predictors of students’ meaningful understanding of meiosis. In another study, Cavallo (1996) explored the relations among students’ meaningful learning orientation, reasoning ability, and acquisition of genetics topics. She reported that meaningful learning orientation best predicted students’ understanding of genetics interrelations, whereas reasoning ability best predicted students’ achievement in solving genetics problems. Cavallo concluded that students’ use of meaningful learning was most important for understanding genetics concepts, whereas formal reasoning was most important for solving genetics problems.

Apart from reasoning ability and meaningful learning orientation, researchers have revealed that achieving meaningful understanding might also require relevant prior knowledge. For example, Haidar (1988) compared high school chemistry students’ applied and theoretical knowledge of concepts on the basis of the particulate theory. He reported that students’ formal reasoning ability and preexisting knowledge played a significant role in their conceptions and use of the particulate theory. Likewise, BouJaoude and Giuliano (1994) demonstrated that prior knowledge, logical thinking ability, and meaningful learning orientation accounted for 32% of the variance in chemistry achievement. Johnson and Lawson (1998) extended the previous studies by examining the relative effects of reasoning ability and prior knowledge on biology achievement in relation to types of instruction. They found that reasoning ability- but not prior knowledge- explained a significant amount of variance in final examination scores in both instructional methods (expository and inquiry).

Researchers’ attention has recently turned toward motivation because of evidence indicating that science teachers may have a more direct influence on their students’ motivation than on cognition (e.g., Anderman & Young, 1994; Pintrich, Marx, & Boyle, 1993). For example, Cavallo et al. (2004) explored the relative predictive influences of learning approaches, motivational goals, self- efficacy, beliefs about the nature of science, and reasoning ability on physics concept understanding and course achievement among male and female students in an inquiry-based physics course. The regression analyses showed that although both self-efficacy and reasoning ability best predicted female students’ concept understanding, only self-efficacy positively predicted male students’ physics understanding. More recently, Kang et al. (2005) examined the relations among 7th-grade Korean students’ cognitive and motivational variables, cognitive conflict, and conceptual change regarding density concepts. The cognitive variables were logical-thinking ability, field dependence or independence, and learning approach, whereas the motivational variables were failure tolerance, goal orientation, and self-efficacy. All cognitive variables and the motivational variables of failure tolerance and self-efficacy were significantly correlated with conceptual change. The results of stepwise multiple regression analysis showed that logical-thinking ability, field dependence or independence, and failure tolerance were statistically significant predictors of conception test scores. The researchers also reported that logical thinking-but not the meaningful learning approach-accounted for the greatest portion of the density conception test score variance in conceptual change classes.

Gender and Science Achievement

In addition to the aforementioned variables, researchers have documented that science is one of the areas in which gender difference is most strongly pronounced. In those studies, however, researchers did not conclusively demonstrate findings on the relation between gender and science achievement. Although some researchers have indicated no significant difference between boys and girls with respect to science achievement (e.g., Dimitrov, 1999; Hupper, Lomask, & Lazarowitz, 2002; Shepardson & Pizzini, 1994; Thompson & Soyibo, 2002; Ugwu & Soyibo, 2004), others have reported significant gender differences (Alparslan, Tekkaya, & Geban, 2003; Cavallo et al., 2004; Soyibo, 1999; Young & Fraser, 1994). For example, Dimitrov indicated no significant difference between girls and boys with respect to achievement in life sciences. Likewise, Ugwu and Soyibo reported no significant gender difference in Jamaican 8th-grade students’ performance on nutrition and plant reproduction concepts. Soyibo, however, demonstrated that girls performed significantly better on a test of errors in biological labeling. In another study, Young and Fraser revealed significant gender differences in biology achievement in favor of boys. Stark and Gray (1999) reported that girls performed at significantly higher levels on tasks in which the content and context were drawn from the biological sciences and on written tasks assessing science skills. However, those researchers found boys to be superior in the physical sciences. In an experimental study, Alparslan et al. explored gender differences in the relative effectiveness of two modes of treatment (conceptual change instruction and traditional instruction) on 11th-grade students’ understanding of respiration. The authors reported a significant difference between girls’ and boys’ performance in favor of the girls, but they found the interaction of treatment with gender difference to be nonsignificant for learning the concepts. They concluded that the mean difference for one factor (gender) does not depend on the levels of the other factor (treatment).

Our review of the related literature provides background information concerning the relations among cognitive variables, motivational variables, and students’ science achievement. In the available studies, researchers have tried to explore contributions of cognitive and motivational variables to college students’ science achievement, mainly in inquiry-based science classes. However, comparative effects of these variables on elementary science achievement with different instructional strategies have not been well documented. For the present study, we chose genetics because of its curricular significance. It is a fundamental part of the science curriculum and is considered an abstract and difficult topic for both students and teachers. Although researchers in several studies discuss the difficulties of teaching and learning genetics, others focus on students’ ideas related to genetics concepts. However, researchers have given less attention to developing strategies to eliminate these difficulties, remediate misconceptions, and improve genetics instruction in elementary science classes. To promote achievement in elementary science classes, it is worthwhile to explore the influence of mode of instruction and motivational and cognitive variables on genetics achievement. Research Questions and Hypotheses

In the present study, we investigated the following research questions:

1. What are the effects of expository instruction and learning cycle instruction on 8th-grade students’ achievement in genetics when pretest, Test of Logical Thinking, Learning Approach Questionnaire, and Self-Efficacy Questionnaire scores are controlled?

2. What is the effect of gender on 8th-grade students’ achievement in genetics?

3. What is the interaction between gender and instructional method?

We hypothesized that (a) learning cycle instruction significantly facilitates achievement in genetics in comparison with expository instruction, (b) there is no significant gender difference with respect to achievement in genetics, and (c) there is no interaction between gender and instructional method.

Method

Sample

Our sample consisted of 213 (89 girls, 124 boys) 8th-grade students who were 13-14 years of age (M = 13.23, SD = 0.42), attending eight whole classes in two public elementary schools in Ankara, Turkey. The schools were in the same school district and were comparable in terms of students’ age, overall grade point average in science, and socioeconomic status. On the basis of their school assessment, participants’ academic performance in science during the regular school year was average.

In Turkish elementary schools, science lessons are compulsory for all students. The science curriculum covers many aspects of chemistry, biology, physics, and geology and is spiraled so that the learners can build progressively on their understanding of concepts. Turkish students receive science instruction as specified in the national curriculum and use the science textbooks approved by the Ministry of Education. The science curricula and the number of school hours devoted to science, therefore, are identical in all elementary schools. Duration of science lessons is four 40-min periods per week, and the language of instruction and materials is Turkish. Teachers generally use an expository method to teach science concepts. Textbooks are the main source of science instruction. The teaching strategies, thus, generally rely on teacher explanation and extensive use of textbooks.

To investigate the effectiveness of the two instructional methods, we randomly chose four whole classes from each school. In each school, we randomly assigned two classes as experimental groups and two as control groups. Therefore, the present study involved a total of four experimental groups (n = 104; 61 boys, 43 girls) and four control groups (n = 109; 60 boys, 49 girls). Three (2 female, 1 male) experienced science teachers gave instructions. For all teachers, the average length of teaching experience was between 18 and 25 years. All teachers had graduated from a university’s science education program. In the present study, each teacher administered both treatments: the learning cycle treatment and the expository treatment. Because none of the schools in the district had adopted the learning cycle as a science teaching approach, we trained teachers about implementation of learning cycle instruction. To discuss implementation of the learning cycle and facilitate the proper use of materials in the learning cycle classes, we gave the teachers two 30-min training sessions prior to the beginning of the study. Meetings with the teachers were held during the study to ensure that they were conducting the treatments in all groups appropriately. Moreover, we contacted teachers several times per week to answer their questions, address problems, or review the treatment procedures. However, teachers widely used expository instruction; therefore, it required no training.

Measures

We used the Genetics Achievement Test (GAT), Learning Approach Questionnaire (LAQ), Test of Logical Thinking (TOLT), and Self- Efficacy Questionnaire (SEQ) for gathering relevant data.

The GAT

To assess students’ genetics achievement, we developed the GAT by examining the related literature. The test assesses understanding of basic concepts in genetics, inheritance, and genetics crosses. It consists of 18 multiple-choice and 2 open-ended questions. The open- ended questions elicit more detailed explanations about related concepts. During the developmental stage of the achievement test, we determined instructional objectives related to the genetics unit by taking the national curriculum into consideration. To ensure that we assessed the students’ achievement properly, we prepared a table of specification. The test items cover knowledge (four items), comprehension (nine items), and application (seven items) levels in Bloom’s taxonomy of educational objectives in the cognitive domain. Figure 1 shows sample items from the test. A panel of four science teachers and three science educators determined the content validity and clarity of each item on the test. The science teachers also analyzed the relatedness of the test items to the instructional objectives. They confirmed that the content validity of the instrument was appropriate for the participants and determined that the GAT was valid with respect to the constructs measured.

Prior to administration, we pilot tested the GAT and made necessary modifications. Teachers administered the final form of the test to students in each group at the beginning of the instruction as a pretest to measure students’ prior understanding of genetics. Teachers readministered the same test to students in each group as a posttest immediately after the instruction to compare the effects of the learning cycle and expository instructions. We rearranged the sequence of the questions from pretest to posttest to minimize the learners’ question recognition.

For scoring purposes, we gave each multiple-choice item a numeric value of 1 if the response was correct or 0 if the response was incorrect. Therefore, scores ranged from 0 to 18. The reliability coefficient, computed by Cronbach’s alpha estimates of internal consistency of the posttest, was .76. Two independent raters evaluated students’ responses to essay-type items. We discussed similarities and differences between the ratings of the two independent raters until we reached a consensus and obtained a relatively high interrater reliability (r = .86).

The TOLT

Tobin and Capie (1981) developed the TOLT to determine the formal reasoning ability of students. The test consists of 10 items designed to measure five formal operational reasoning modes: proportional reasoning, controlling variables, probability reasoning, correlational reasoning, and combinational reasoning. The 10 items include two parts: an answer and a reason for the selected answer. For the response to be considered correct, students have to respond correctly to both parts. Therefore, the maximum test score on the TOLT is 10 points. Geban, Askar, and Ozkan (1992) translated and adapted this test into Turkish. Internal consistency for this study was .73.

The LAQ

The LAQ is a 22-item, 4-point Likert instrument that was designed to measure students’ orientations to learning ranging from meaningful to rote (Cavallo, 1996). Students responded to each statement by indicating their agreement, ranging from A (always true) to D (never true). We reverse-scored rote scores from the LAQ so that a high score showed a more meaningful learning orientation and a low score showed a more rote-learning orientation. We translated and adapted the questionnaire into Turkish. Two independent science educators fluent in English verified the translation of the items. We pilot tested items in the questionnaire and found them to be comprehensible to 8th-grade Turkish students. For the present study, we calculated Cronbach’s alpha for the LAQ as .85. Figure 2 presents sample items from the LAQ.

The SEQ

We used the SEQ scale of the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich & De Groot, 1990) to measure students’ self-efficacy. The SEQ includes nine items concerning self- perceived competence and confidence in performance of class work. A high self-efficacy score indicates a high self-efficacy or confidence in one’s ability to learn science. Sungur and Tekkaya (2006) translated and adapted the questionnaire into Turkish. In the present study, we calculated the Cronbach’s alpha as .86. Although the MSLQ was developed for college students, researchers have used it successfully with elementary (Garcia & Caso, 2006; Kang et al., 2005; Pintrich, Anderman, & Klobucar, 1994; Shih, 2005) and high school students (Barlia & Beeth, 1999; Higgins, 2000; Sungur & Tekkaya).

Research Design

We adopted the nonequivalent control group design as a type of quasiexperimental design. The random assignment of already formed classes to experimental and control groups and administration of pretest and posttest to each group characterizes this design. We used whole classes because it would be too disruptive to the curriculum and too time-consuming to take students out of their classes for instruction. Moreover, because of administrative rules, we chose the classes-but not the students-randomly.

Procedure

Pinar Dogru Atay visited the schools after getting permission from the administration. Approximately 2 weeks before the treatment started, she administered the pre-GAT, TOLT, LAQ, and SEQ to the participants. She informed the students about the purpose of the questionnaires. After this short explanation, she asked the students to complete the questions on their own. She instructed them to think about each question and answer it as it applied to them. After the treatment, she gave the students the same GAT as a posttest.

We examined three cognitive variables (learning orientation, reasoning ability, prior knowledge) and one motivational variable (self-efficacy). We conducted a two-way analysis of covariance (ANCOVA) to analyze the data obtained from the test scores. The ANCOVA in this study primarily reduced the error variance, partitioning out the variance attributed to the covariates (Hinkle, Wiersma, & Jurs, 1988). In this analysis, we considered methods of instruction (learning cycle, expository) and gender (boy, girl) as the independent variables. Posttest scores served as dependent variables. We covaried the effects of students’ pre-GAT, TOLT, LAQ, and SEQ so that their effect on students’ achievement could be controlled. We performed the analysis with the significance level of .05 by using SPSS. Nature of Instruction

The instruction for each group spanned 4 weeks and addressed a unit on genetics. Four experimental groups and four control groups took part in the study. Teachers instructed the experimental groups by using the learning cycle method. We assigned the other classes as control groups, and teachers instructed them by using the expository method. Teachers covered the topics related to genetics as part of regular classroom curriculum in the science course.

Expository instruction, a teacher-directed strategy, represented the customary approach in the regular science course. In regular classroom instruction, the teacher provided instruction through lecture and discussion to teach concepts. He or she structured the entire class as a unit, wrote notes on the chalkboard about the definition of concepts, and passed out worksheets for students to complete. The teacher defined and described each concept in the order in which it appeared in the textbook. Students took notes throughout the lesson. After the teacher’s explanation, he or she led class discussion of the concepts by teacher-directed questions. The teacher generally directed questions to the whole class and discussed the answers with the students. The teacher devoted the majority of class time to instruction and discussion stemming from his or her explanation and questions. The remaining time was used by the worksheet study and solving problems. The teacher used worksheets as practice activities that required written responses and reinforced the concepts presented in the classroom sessions. The lesson ended with the students’ answering the worksheet questions orally and completing the worksheet together. The teacher collected and corrected these worksheets, and the students reviewed them after correction. The main idea behind this teachercentered instruction was to provide students with clear and detailed information. They had little opportunity to engage in inquiry, work in small groups, manipulate objects, or discuss their opinions with classmates. The teacher used discussion to verify information from the textbook. He or she initiated the discussion and determined appropriate questions.

When concepts were presented and learned through the learning cycle, the role of the teacher, the role of the students, and the aims of course changed significantly from those of expository instruction. Teachers adopted the role of the facilitator. Such classes are characterized by active student involvement in completing an investigation, answering questions, presenting the results to others, and interacting with each other and with their teachers. In the present study, teachers developed learning cycle lessons about the passing of traits and genetics crosses. During learning cycle instruction, teachers gave students working in small groups two beakers containing equal amounts of red beans and white beans that symbolized the alleles of the mother and the father, respectively. Each beaker represented a parent organism. Teachers also provided students with a worksheet for gathering and recording data about the concept to be learned. In the first part of the activity, each group used one beaker filled with equal numbers of red and white beans to realize that alleles separate randomly so that each gamete receives one or the other with equal likelihood. Each group pulled out one bean at a time and recorded the data (red or white) on the worksheet. In the second part, each group used two beakers, pulling out one bean at a time from each beaker and recording the observations (red or white) on another worksheet. Hence, the groups observed the ratio of genotypes when two heterozygous individuals were crossed. Then they discussed their observations and ideas regarding the results of the activities with their peers. In the concept introduction phase, the teacher initiated a discussion environment. The teacher asked students to report their data to the class and to interpret their findings. In this way, students attempted to give meaning to their observations. Teachers then requested that students express the main idea behind their observations. After accepting all students’ ideas, the teacher introduced related concepts: the independent assortment rule, monohybrid cross, and Punnett square. Later, students compared expected ratios with observed ratios. In the concept application phase, students applied the newly learned concepts to a problem situation. In the problem, the teacher asked them to predict the ratio of genotypes for pea plants with different genotypes. They used a Punnett square to identify genotypes of the individuals and discussed patterns of inheritance with the intention of deepening their understanding.

In the study, the teacher gave each group an equal amount of instructional time and provided them with the same materials and assignments, except the learning cycle materials. To control the teacher effect and bias, the two independent observers observed all class activities of each group. In addition, we developed a checklist to evaluate whether the treatments were implemented as intended. Analysis of these data revealed that the treatments were different for experimental and control groups. For example, in the learning cycle groups, students made observations, gathered data, discussed with peers, constructed hypotheses, and found answers to their questions. They linked the new concepts to what they already knew. However, in the expository groups, the teacher explained the concepts by using the science textbook. Students rarely asked questions and were rarely involved in teacher-led discussions. Students generally took notes and solved problems related to the target concept. Thus, we concluded that each teacher delivered each treatment properly.

Results

Table 1 shows the means and standard deviations of the study variables. Students in each instructional group had a high mean score on the LAQ, indicating that participants of this study generally used meaningful learning approaches rather than rote- learning approaches. However, the mean scores on the TOLT, SEQ, and the pretest reflected that students in each group had a medium level of formal-reasoning ability, slightly high self-efficacy, and inadequate relevant prior knowledge, respectively.

To establish if there were significant differences in students’ posttest means attributable to treatment effect, we computed a two- way ANCOVA by using their pretest, reasoning ability, learning approach, and self-efficacy scores as covariates to reduce the error variance, partitioning out the variance attributed to the covariates. Because a number of issues and limitations are associated with ANCOVAs (Pallant, 2001; Tabachnick & Fidell, 1996), we checked key assumptions of the ANCOVA such as linearity, homogeneity of regression, homogeneity of variance, and normality before conducting the ANCOVA. We used scatterplots to check the linearity assumption for each of the groups (that is, the different levels of the independent variables). Moreover, we calculated the coefficient of determination (r2) for different levels of the independent variables to determine the strength of the relations among students’ posttest scores and covariates. Coefficients of determinations for pretest, reasoning ability, self-efficacy, and learning approach were, .51, .48, .32, and .30, respectively. To test whether there was a violation of the assumption of homogeneity of regressions, indicating an interaction effect between the covariates and the factors, we analyzed a model with all main effects of the factors and covariates and the interaction of the covariates with the factors. We observed nonsignificant interaction effects, F(12, 193) = 1.51, p = .124, so that the homogeneityof- regressions assumption was met. In addition, results of Levene’s test of equality of variances showed that variability of the scores for each group was similar, F(3, 209) = 2.548, p = .057. Moreover, skewness and kurtosis values showed that there was no violation of the normality assumption.

Table 2 summarizes the two-way ANCOVA comparing the mean posttest scores of the performance of students in both the experimental and control groups. The findings revealed that the main effect due to treatment was significant, F(1, 205) = 63.62, eta^sup 2^ = .24, p < .05, indicating that there were significant differences in the students' posttest scores in favor of the experimental group. The relation between the treatment and the posttest score was strong, eta^sup 2^ = .24 (Green, Salkind, & Akey, 2000). The students in the experimental group who were engaged in learning cycle instruction demonstrated better performance (adjusted M = 13.03) than did the control group students who were engaged in expository instruction (adjusted M = 9.98). With 95% confidence, we found that the average increase in students' achievement was 11.52 +- 0.36. The analysis also yielded statistically significant contributions for the covariates TOLT score, F(1, 205) = 66.06, eta^sup 2^ = .24, p < .05, and LAQ score, F(1, 205) = 35.36, eta^sup 2^= .15, p < .05, meaning that 24% and 15% of the variance in student genetics achievement were explained by students' reasoning ability and meaningful learning orientation, respectively. However, we found no significant contribution of the covariates pretest and self-efficacy scores to students' genetics achievement. Concerning gender difference, the data indicated no significant difference between the performances of boys and girls, F(1, 205) = .41, p > .05. In addition, no significant interaction between treatment and gender on genetics achievement was demonstrated, F(1, 205) = .17, p > .05. These results imply that students in the experimental group who engaged in the learning cycle instruction achieved greater improvement than did those in the control group. We evaluated the average percentage of students in both experimental and control groups who selected the correct answer for both the preand post-GATs. The average percentage of correct student responses rose from 32.7% to 70.8% in the experimental group, yielding a gain of 38.1%, whereas the percentage of correct student responses increased from 27% to 44% in the control group, yielding a gain of 17% after treatment. Overall findings reveal that expository instruction has limited success in promoting a meaningful understanding of the target concepts in comparison with learning cycle instruction.

Discussion

In this study, we examined the effectiveness of two instructional methods- learning cycle and expository instruction-on 8th-grade students’ achievement in genetics. We covaried the effects of students’ reasoning ability, meaningful learning orientation, prior knowledge, and self-efficacy so that any significant differences in achievement between the groups could be attributed to the instruction. As hypothesized, learning cycle instruction, compared with expository instruction, caused a statistically significantly better acquisition of scientific concepts related to genetics. This result is consistent with previous studies’ results (Abraham & Renner, 1986; Balci et al., 2006; Cavallo, 1996; Lawson, 2001; Lawson, Alkhoury, Benford, Clark, & Falconer, 2000; Marek et al., 1994).

The significant difference in experimental group students’ performances could be attributed to various direct experiences that gave participants the opportunity to question and formulate problems, manipulate materials, observe and record data, and reflect on and construct knowledge from the data. The learning cycle, by reflecting scientific inquiry processes, allowed students to become active participants in the process as they constructed an understanding of scientific concepts. Moreover, because of the potency of the learning cycle, students see the links among concepts explicitly and connect newly learned concepts to ones they already possess. The concepts of genetics include many interrelated ideas and facts. To achieve meaningful understanding of genetics, learners must actively relate the ideas and facts that make up the concept. The strategies used in learning cycle classes supported a change in students from passively receiving information to actively examining their own conception. Students in the learning cycle classrooms were involved in activities that helped them reorganize their prior knowledge. They were allowed to think about their prior knowledge and reflect on it. This procedure helped students to learn meaningfully by making connections among concepts and by developing reasoning skills. In the present study, during the expository lesson, the teacher connected ideas for the learners. However, in the learning cycle classes, students made the connections among the concepts by themselves through explorations and discussion. The important part in implementing the learning cycle instruction was the intensive teacher-student and student-student interaction because it provided students more time to discuss their findings with both their teacher and their peers. Barman et al. (1996) attributed the success of the learning cycle to providing opportunities for student interaction and dialogue through systematic instruction, learning experiences, and activities in each of the well-known phases. According to Cavallo, Miller, and Saunders (2002), such a discussion environment facilitates students’ understanding, encourages their conceptual restructuring, and provides opportunities for greater involvement, thereby giving students more chances to gain insights and develop intrinsic interest. As a result, learning cycle instruction allowed students to learn genetics by performing experiments, drawing conclusions, solving problems relevant to their experiments, and constructing concepts meaningfully.

Our study showed that, among the variables examined, reasoning ability and meaningful learning approach each accounted for a significant portion of the students’ performance on the GAT. This finding means that students who were attempting to learn genetics by linking concepts and had high reasoning ability had greater achievement in genetics. These findings may lead to a conclusion that lack of formal reasoning and use of rote learning can be possible causes of low student achievement in genetics. These results are consistent with previous researchers’ findings in which reasoning ability and meaningful learning approach were important predictors of science achievement (e.g., Bitner, 1991; BouJaoude et al., 2004; Cavallo, 1996; Johnson & Lawson, 1998). For example, Lawson and Thompson (1988) pointed out the importance of level of reasoning ability in students’ understanding of genetics concepts and supported the idea that formal reasoning is essential for elimination of some misconceptions in biological concepts. Likewise, Lawson and Renner (1975) argued that solving and interpreting genetics problems involves formal level operations and that low- formal thinkers encounter learning difficulties when they are asked to study concepts that require formal reasoning. However, participants in the present study have not fully developed formal thought. Still, many genetics concepts were identified as on an abstract level in science curricula and required formal reasoning (Lawson & Renner; Smith & Sims, 1992). Therefore, students’ ability to cope with formal concepts, such as those found in the study of genetics, in a meaningful manner is correlated with their level of intellectual development (Lawson et al., 2000; Lawson & Renner). Williams and Cavallo (1995) claimed that when concrete operational individuals try to learn formal concepts, a mismatch occurs between the learners’ reasoning ability and the need to comprehend the concept. Therefore, matching the concept with the reasoning ability of the learner may be the vital step to greater achievement.

In this study, prior knowledge and self-efficacy did not predict students’ genetics achievement. This finding was consistent with some of the results reported in the literature (Johnson & Lawson, 1998; Kang et al., 2005; Mitchell & Lawson, 1988). For example, Mitchell and Lawson reported that although students’ reasoning level was the most important predictor of performance, prior knowledge did not significantly account for variance on a GAT. Likewise, Johnson and Lawson found that although reasoning ability was a significant predictor of achievement in inquiry classes, prior knowledge was not a significant achievement predictor in either instructional method (inquiry vs. expository). The present study indicated that prior knowledge did not contribute to students’ genetics achievement. It is noteworthy that with 20 possible correct responses on the pretest, participants attained a relatively low mean score (see Table 1). This means that the participants responded correctly to less than 50% of the questions, indicating a low level of relevant prior knowledge of genetics concepts. If researchers take the participants’ poor pretest performance into consideration, the failure of prior knowledge to predict genetics achievement is not surprising.

The present study also fails to indicate a significant contribution of self-efficacy to genetics achievement. Students who were confident in their ability to successfully learn genetics were not necessarily successful in this topic. This result is not consistent with the results that Cavallo et al. (2004) and Cavallo et al. (2003) obtained: For college biology students, self-efficacy significantly predicted female and male students’ physics understanding and course achievement, and motivation to learn for the sake of learning was more important for course achievement than was reasoning ability. However, Kang et al. (2005) indicated that self-efficacy was not significantly related to the density conception test scores in computer-assisted instruction. A possible explanation for the discrepancy between these findings may lie in the different types of instruction, the nature of concepts instructed, and the context-specific nature of self-efficacy (Pintrich et al., 1993).

Instructional treatments, as suggested by previous researchers, may have had different effects for girls and boys because of the differential motivation and interest (Chambers & Andre, 1997; Wang & Andre, 1991). The present study indicated no significant mean difference between girls and boys with respect to genetics achievement and no significant interaction between gender and treatment, p > .05. These findings suggest that learning cycle instruction is likely to be effective for teaching both boys and girls in genetics. The learning cycle instruction created such a learning environment that both girls and boys could find equal opportunities to practice with the materials, engage in discussion, and interact with their peers and teachers. The result of the present study, thus, supports the idea that inquiry-based instruction has the potential for equitable outcomes (Shepardson & Pizzini, 1994). Further research, however, is necessary to clarify this finding.

Limitations and Recommendations The present study has limitations for researchers to consider in any attempt to generalize the results. We conducted this investigation at public schools in an urban area by using whole classes. Data from other school districts and from different school types might provide different results. This study was limited to a genetics unit and 213 eighth-grade students. Therefore, our findings should be viewed with caution.

Because of these limitations, we can recommend further research. For example, a retention test could be administered to determine whether the strategy used produced long-term retention. The learning cycle instruction would be implemented during a whole semester so that other science topics could be included. Reasoning ability, learning orientation, and self-efficacy scales could be readministered at the end of the semester to detect if there is any improvement in these variables with respect to the mode of instruction. A qualitative analysis of prior knowledge could be conducted to shed more light on students’ initial conceptual structure. Motivation constructs such as performance-goal orientation, learning-goal orientation, and avoidance goals could be examined as additional variables.

Conclusion

Overall, the findings of this study further support the use of the learning cycle in both research and teaching. Findings might be useful for improving classroom practices in teaching science concepts and for the development of suitable materials promoting students’ understanding of science. The present study implies that learners need to have a certain level of abstract thinking and meaningful learning orientation to comprehend genetics topics. During genetics instruction, therefore, teachers should provide a learning environment that fosters scientific reasoning and encourages students to develop a meaningful learning approach. This study suggests the use of the learning cycle as a teaching strategy that promotes this effort. We hope that this investigation will serve as a motivating force for further interest and research in the effectiveness of learning cycle instruction on students’ understanding of science concepts.

1. Where are genes located?

(a) cell

(b) nucleus

(c) chromosomes

(d) DNA

2. If a family had three sons in a row, what is the probability that the fourth child would be male?

(a) 1/2 (b) 1/3 (c) 1/4 (d) 2/3

3. In peas, round seed (W) is dominant to wrinkled seed (w). Which of the following crosses yielded 4 wrinkled and 12 round offspring?

(a) WW x ww b) Ww x Ww (c) Ww x ww (d) Ww x WW

FIGURE 1. Sample test items used in the Genetics Achievement Test.

1. As I am reading new materials in science, I try to relate what I already know on the topic.

2. I generally put a lot of effort into trying to understand things that at the beginning seem difficult.

3. I learn things by rote, going over and over them until I know them by heart.

FIGURE 2. Sample items from the Learning Approach Questionnaire.

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