Inquiry-Based Instruction: A Possible Solution to

Int J of Sci and Math Educ DOI 10.1007/s10763-016-9718-x

Inquiry-Based Instruction: A Possible Solution to Improving Student Learning of Both Science Concepts and Scientific Practices Jeff C. Marshall 1 & Julie B. Smart 1 & Daniel M. Alston 1

Received: 21 August 2015 / Accepted: 3 February 2016 # Ministry of Science and Technology, Taiwan 2016

Abstract The current study, involving 219 teachers and 15,292 students, examined the relationship between teacher participation in a sustained professional development intervention designed to improve the quantity and quality of guided inquiry-based instruction in middle school science classrooms and subsequent student academic growth. Utilizing a quasi-experimental design, the growth scores of students of participating and non-participating teachers were compared to a benchmark measure established by a virtual comparison group (VCG) of similarly matched students. The results indicate that for all three MAP tests (Scientific Practices, Science Concepts, Science Composite) the students of participating teachers had significantly higher than expected growth relative to the VCG when compared to students of non-participants. In addition, students of teachers who participated in the PD intervention consistently exceeded the growth expectations of the benchmark VCG by up to 82 %. This study supports prior research findings that inquiry-based instruction helps improve students’ achievement relative to scientific practices and also provides evidence of increasing student conceptual knowledge. Keywords Inquiry-based instruction . Inquiry learning . Science education . Scientific practices . Student achievement Data from national and international metrics confirm that American students continue to underperform in K-12 science classrooms (Martin, Mullis, Foy & Stanco, 2012; Schmidt, McNight & Raizen, 2002; U.S. Department of Education [DoED], Institute of Education Sciences [IES] & National Center for Education Statistics [NCES], 2011).

* Jeff C. Marshall [email protected]

1

Clemson University, Clemson, SC, USA

J. C. Marshall et al.

Among these measures, the 2011 National Assessment of Education Progress (NAEP) shows that only 34 % of 8th grade students earned proficient or above (DoED et al., 2011) in measures of scientific processes and content. To further highlight the urgency of this situation, the performance expectations outlined in the Next Generation Science Standards (NGSS) have raised the expectations for all students (Achieve, 2013; Marshall & Alston, 2014). Thus, effective professional development (PD) is vital to insuring that teachers are adequately prepared to meet the needs of today’s learners. Currently, students infrequently engage in meaning making and reasoning, and they rarely investigate their own questions or ideas (Kane & Staiger, 2012). Further, the teacher remains the center of the class (lecturer, explainer, leader of discussions) where students largely act as the recipient of knowledge (note taking, performing prescriptive lab to confirm what has been previously told, responding to didactic questions) (Weiss, Pasley, Smith, Banilower & Heck, 2003). Effective PD provides a potential solution to align instruction with reform-based initiatives, such as inquiry-based instruction, that seek to improve student achievement while engaging students deeply in the learning of science.

Literature Review and Theoretical Framework Why Inquiry? NGSS establishes higher expectations for science education at the various grade bands as compared to its predecessor the National Science Education Standards (NSES) (Achieve, 2013; National Research Council [NRC], 1996, 2012). Although NGSS does not explicitly address inquiry instruction in the performance expectations (as with NSES), the expectations outlined in NGSS, along with the new state standards for states not adopting NGSS (e.g., SC State Science Standards), imply that inquiry is a necessary component of the learning process. The recommendations from A Framework for K-12 Science Education (NRC, 2012) served to guide the development of the performance expectations found in NGSS, and within these recommendations are the scientific and engineering practices (e.g., planning and carrying out investigations, developing and using models, analyzing and interpreting data, engaging in argument from evidence) that provide a compelling argument for inquiry-based instruction. Although the performance expectations stated in NGSS (and in other state standards) may support and advocate for inquiry-based instruction, there currently is a disconnection between expected teacher practices and what actually occurs in the classroom (Capps, Crawford & Constas, 2012). Contemporary models of inquiry-based instruction (e.g., 5E, 4E × 2) build on the Piagetian notion of cognitive dissonance and emphasize the importance of scaffolding student learning throughout the instructional process which is a central notion of social constructivism (Vygotsky, 1978). At the core of inquiry-based instruction, the learner must have the opportunity to explore concepts before formal explanations of the phenomena are provided, thus facilitating conceptual understanding (Bransford, Brown & Cocking, 2000; Bybee et al., 2006). This explore before explain pattern is consistent in all majorly accepted inquiry-based instruction models such as the 4E × 2 Instructional Model that adopts the core of the 5E Model (Engage, Explore, Explain, and Extend) while

Inquiry-Based Instruction

explicitly linking formative assessment and teacher reflection (thus the B× 2^) to each phase of inquiry instruction (Marshall, Horton & Smart, 2008). Reform movements continue to support constructivist instructional approaches, such as inquiry-based instruction, typified by student-centered classroom practices (Bybee et al., 2006; Marshall, 2013; Vygotsky, 1978; Windschitl, 2008). In addition, research provides evidence of a relationship between inquiry-based instruction and positive student outcomes in science. Positive effects on both student cognitive and attitudinal outcomes in science have been associated with inquiry-based instruction (Cheng, Wang, Lin, Lawrenz & Hong, 2014; Marshall & Alston, 2014). Specifically, significant positive correlations were noted between student achievement in science and aspects of inquiry such as drawing conclusions from data and conducting scientific investigations. In addition, significant engagement with inquiry-based instruction in science has demonstrated positive outcomes in student attitudes towards science (Cheng et al., 2014). Student argumentation and retention of science content also show positive associations with inquiry (Johnson, 2009; Wilson, Taylor, Kowalski & Carlson, 2009). Another critical finding suggests that inquiry-based instruction may play a role in narrowing the achievement gap in science achievement (Geier, Blumenfeld, Marx, Krajcik, Fishman, Soloway & ClayChamber, 2008; Marshall & Alston, 2014), thus supporting the argument that inquiry is beneficial for all learners. Inquiry Defined While science educational reformers have continued to promote the merits of inquirybased instruction, consensus has been lacking for a single definition (Bransford et al., 2000; Bybee et al., 2006; NRC, 1996, 2012; Supovitz, Mayer & Kahle, 2000). Perhaps part of the reason for this lack of consensus is because key documents that guide science education practice such as NSES are inconsistent in how they define inquiry. For instance, BScientific inquiry refers to the diverse ways in which scientists study the natural world…^ and then later on the same page, Binquiry is the multifaceted activity that involves making observations; posing questions…^ (NRC, 1996, p. 23). Based on the vision stated in NGSS, along with previously cited instructional models, and prior definitions such as those mentioned above, we operationalize proficient inquirybased instruction in science education as an intentional student-centered pedagogy that challenges the learner to explore concepts, ideas, and/or phenomena before formal explanations are provided by the teacher and/or other students. Within this definition of inquiry, students engage with one or more of the scientific practices (as defined by NGSS) while studying one or more science concepts (as defined by NGSS). Note that the target for proficiency seeks to have students explore before explanation versus confirming explanations through activities or labs after the fact—this latter scenario is ubiquitous in our classrooms and curriculum guides and tends to impede use of the scientific practices (e.g., design an investigation to test a hypothesis). Professional Development and Inquiry While reform movements continue to support constructivist instructional approaches (Bybee et al., 2006; Marshall, 2013; Vygotsky, 1978; Windschitl, 2008), numerous challenges continue to hinder efforts to promote and support inquiry-based instruction.

J. C. Marshall et al.

Some of these challenges include the following: (1) personal belief structures counter to reform efforts (Banilower, Smith, Weiss, Malzahn, Campbell, & Weis, 2013; Keys & Kang, 2000; Marshall, Horton, Igo & Switzer, 2009; Wallace & Kang, 2004), (2) cultural beliefs that encourage transmission of knowledge and efficiency in learning over depth of learning (Tobin & McRobbie, 1996), (3) low teacher self-efficacy for implementing reform-based instruction (Woolfolk, 2004), (4) insufficient pedagogical content knowledge (PCK) (Garet, Porter, Desimone, Birman & Yoon, 2001; Shulman, 1986), and (5) deficits in curricular and administrative support (DuFour, DuFour, Eaker & Karhanek, 2004; Spillane, Diamond, Walker, Halverson & Jita, 2011). Though this list is not exhaustive, it does identify research-based components that have been identified as factors relating to lasting reform. Among the challenges associated with implementation of inquiry-based instruction and the more rigorous NGSS is the issue of assessment. Since the NGSS places a strong emphasis on higher-order thinking skills, assessments to measure these skills will differ from past measures (Achieve, 2013; NRC, 2012). Previous assessments of students’ scientific knowledge have generally focused on lower-order skills which could be measured using items focused on recall of factual information or basic application of scientific content (Krathwohl, 2002). An analysis of a state ranked by the Fordham Institute (Gross, Goodenough, Lerner, Haack, Schwartz & Schwartz, 2005) as having among the strongest science standards revealed that 82 % of high school life science standards were written at a low cognitive level, requiring students to only recall and understand basic scientific knowledge (Marshall & Alston, 2014). The NGSS stresses student mastery of higher-order scientific practices such as evaluation and analysis; these skills place greater weight on student thinking and will require a greater complexity in science assessment. Since high-stakes assessments generally drive a wide range of educational practices, these new science assessments will necessitate a shift in the way scientific concepts and processes are taught; PD will be a key piece in preparing teachers to meet the expectations of the NGSS and associated student achievement measures. Regardless of the barriers or challenges in adopting reform approaches in science education, PD interventions become a critical component in preparing teachers to implement effective inquiry-based instruction. In essence, PD interventions are considered effective if they are able to create lasting change among the participants and those in their realm of influence (e.g., students, other teachers, leaders). Although our understanding is limited regarding which PD interventions generate significant and lasting change among students, several studies report less than ideal results: (1) of the 90 % of teachers who have engaged in PD, the majority reported it to be useless (DarlingHammond, Chung - Wei, Andree & Richardson, 2009); (2) short-term workshops do not change teacher practice and have no effect on student achievement (Yoon, Duncan, Lee, Scarloss & Shapley, 2007); and (3) teachers need sustained support because first attempts at reform typically fail (Blank, de las Alas & Smith, 2008). Further, few studies are able to link teacher instructional change to student achievement. Specifically, Shymansky, Wang, Annetta, Yore and Evertt (2012) were only able to identify three funded NSF local and systemic change studies that reported student achievement data (Czerniak, Beltyukova, Struble, Haney & Lumpe, 2005; Johnson, Fargo & Kahle, 2010; Revak & Kuerbis, 2008), and only one of those (Revak & Kuerbis, 2008) used high stakes test results. The present study seeks to link a PD intervention for middle school science teachers to the resulting student growth achieved during the academic year.

Inquiry-Based Instruction

To achieve significant and lasting change, the PD intervention reported in this study was conceptually modeled on best practice research models that support teacher transformation (Banilower, Boyd, Pasley & Weiss, 2006; Darling-Hammond, 2000; Loucks-Horsley, Stiles, Mundry, Love & Hewson, 2010; Supovitz & Turner, 2000). Among these models is the general consensus that PD needs to be substantial in duration (80+ hours) (Darling-Hammond et al., 2009; Supovitz & Turner, 2000), sustained over a significant period of time (ideally a year or more), and be highly supportive and highly adaptive to the needs and concerns of the participants. The research questions that guide this study include the following: (1) To what extent does the professional development (PD) intervention, which is designed to increase the quantity and quality of inquiry-based instruction, improve growth scores of students of participants above students from the virtual comparison group (VCG) and from students of non-participating teachers? (2) Is student growth (above what would be expected) dependent on the number of years that participating teachers were involved in the PD intervention? Because of the PD focus on improving inquiry-based instruction, we anticipated seeing student growth above the expected for scientific practices (as defined by NGSS). However, we did not know the degree to which this intervention would translate to improved growth for science concepts (as defined by NGSS).

Methods Study Design This study, conducted over a 6-year time period, follows a quasi-experimental research design, including three levels of PD participants and one non-participant group. The three levels of PD participants include (1) Year 1 PD participants, (2) prior PD participants who previously completed one full year of PD involvement but did not continue for a second year, and (3) Years 2 and 3 PD participants. The non-participant group entailed a group of non-PD participants from the same school/school district. The four groups (three PD participants and one non-participant) were compared to the expected growth performance generated by the benchmark Virtual Comparison Group (VCG) measure. Student growth was measured using three science tests (Scientific Practices, Science Concepts, and Science Composite that averages the two). The faculty involved who facilitated this intervention are part of Inquiry in Motion Institute that has the primary goals of improving the quantity and quality of inquiry-based instruction and improving student achievement in science. The VCG, which establishes a benchmark measure of expected student growth, is generated by a software program drawing from a database of all students who completed the Measures of Academic Progress (MAP) in the state during the identical testing windows. The MAP test is the measure of science content and scientific practices in the current study and will be discussed below. The VCG score was computed by matching all students in the current study (N = 15,292) with similarly matched students from around the state. Each of the VCG students were matched to a student in the study according to race, gender, same Fall MAP score, same test window from Fall to Spring, and similar school free and reduced lunch rate. This process allows

J. C. Marshall et al.

for a comparison of the growth of the PD and non-PD groups with the expected growth of similar students in the state with similar demographics. This measure allows us to analyze additive growth effects of the PD for participating teachers versus the growth that would normally be expected for these students. This expected growth score is generated by an algorithm produced by the Northwest Evaluation Association (NWEA), the creators and administrators of the MAP measure. The VCG students are not included in the number of reported student participants (N = 15,292) for this study. Participants Five school districts, 10 schools, 219 teachers, and 15,292 students participated in this study. Table 1 provides a summary of the district, school, teacher, and students of study participants disaggregated by project intervention year. Teachers were recruited from high-needs/low-performing schools based on our state rating. In cases, when we could not secure enough participation from the highest-needs schools, we opened the program up to other schools in the targeted districts. According to data reported by the National Center for Educational Statistics, our state had a relative school poverty index of 47.3– 52.5 % during the years of this study (the rate continued to increase—possibly attributed to the poor economy in the state and nation at the time). The schools that participated in this intervention had an average poverty index of 58.5 % (range of 39.0– 94.0 %). This index was determined by the percentage of students qualifying for free and reduced lunch. These figures are important for establishing that participating schools were typically composed of high needs populations, and these data later become relevant for the student matching associated with the Virtual Comparison Group. PD Intervention Overview Participation from 60 % of the science teachers at a given school was required in order to establish a PD partnership with the school. Commitment included at least 80 hours per year of involvement. These hours included the following: (1) two full weeks of whole group summer interactions, (2) four whole group follow-up meetings during the academic year, (3) at least four individual classroom observations with Inquiry in Motion faculty, and (4) individual meetings with participating teachers to discuss progress, challenges, and next steps. While the overall program was very collaborative, Table 1 Overview of participants for 6 years of program Intervention year

N districts

N schools

N teachers

N students tested

Non-participant

4

6

123

7468

Year 1 Participant

5

10

47

3805

Past Participant

4

8

17

1475

Year 2 or 3 Participant

5

9

32

2544

Total

5

10

219

15,292

Inquiry-Based Instruction

Year 1 interactions focused on improving individual instructional practice. Year 2 and 3 participants still sought to continue improving individual practice, but they were also required to design and facilitate targeted initiatives in their school or district to improve inquiry-based instruction. Because administrative support and leadership is critical, administrators were included in the initial school meeting to recruit teachers, during a portion of the summer intervention, and through program summaries and visits from Inquiry in Motion faculty during the year. To provide greater support for teachers, three unique components (the 4E × 2 Instructional Model, the Dynamic Webtool, and EQUIP) were central to the PD and thus need further clarification. First, the 4E × 2 Instructional Model deviates from the widely adopted 5E Model (Bybee et al., 2006) primarily in an effort to make the 5th E (Evaluate) more explicitly distributed throughout the learning process and not just at the end of the lesson or unit as many interpret. The 4E × 2 Instructional Model adopts the core of the 5E Model (Engage, Explore, Explain, and Extend) while explicitly linking formative assessment and teacher reflection (thus the B× 2^) to each phase of the inquiry instruction (Marshall et al., 2008). We know that embedding formative assessment throughout learning can assist in narrowing the achievement gap (Black, Harrison, Lee, Marshall & Wiliam, 2004). Further, when teachers are deeply reflective about their instructional practice, learning can be heightened and targeted to the learner’s needs (National Board for Professional Teaching Standards, 1994, 2000). The second support, the Dynamic Webtool, provides an innovative technological development designed to encourage, guide, and maintain the desired teacher transformations. The Webtool allows teachers to view inquiry-based exemplars (lesson typically lasting two to five class periods) that have been created by other educators, modify existing exemplars to meet individual needs, create new inquiry-based exemplars using the online template, and share exemplars with others. Similar to the Japanese Lesson Study, the exemplars emphasize collaboration as teachers create, test, refine, and share lessons (Lewis, 2002; Puchner & Taylor, 2006; Stigler & Hiebert, 1999). The Webtool is dynamic because authors can edit and refine lessons in their own workspace. The Webtool helps provide a template for guiding the conversations as teams develop new exemplars that address specific concepts and practices. The third support, EQUIP, provides both a valid and reliable tool to measure teacher performance during observations, but it also provides a descriptive rubric to help teachers target steps that can be taken to improve practice (Marshall et al., 2009). The development of EQUIP was greatly informed by two extensively researched observation protocols (Henry, Murray & Phillips, 2007): The Reformed Teaching Observation Protocol (Sawada, Piburn, Judson, Turley, Falconer & Russell, 2000) and Inside the Classroom Observation and Analytic Protocol (Horizon Research, 2002). Though highly reliable and often used by others, neither protocol was determined to be solely valid for measuring the quantity and quality of inquiry-based instruction transpiring in classrooms. The resulting instrument, which has been used in over 2000 known full class observations, contains 19 indicators divided among four constructs: (1) Instruction (e.g., teacher role, order of instruction); (2) Discourse (e.g., questioning ecology, classroom interactions); (3) Assessment (e.g., prior knowledge, assessment type); and (4) Curriculum (e.g., content depth, standards). Each indicator contains a descriptive rubric to guide teachers through the four possible levels (PreInquiry, Developing, Proficient, Exemplary). The instrument was standardized so that

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Level 3 (Proficient) is the target for each indicator. The Webtool and EQUIP both support the framework of the 4E × 2 Instructional Model. Collectively, all three supports provide a seamless support for teachers. Measures: MAP Test The MAP Science Test, published by NWEA, was used to measure the growth of students relative to a VCG. MAP provides a reliable and valid assessment of student knowledge associated with Science Concepts and Science Practices and is used by schools in 48 states (Northwest Evaluation Association, 2004). MAP is an adaptive test that provides more or less challenging items, depending on students’ success or failure on previous questions. MAP’s inherent strengths include the following: (1) items are aligned to science standards, thus providing a high predictive validity for state assessments (Cronin, Kingsbury, Dahlin, Adkins & Bowe, 2007; Northwest Evaluation Association, 2005); (2) the adaptive nature of the test provides a broader, more robust sample of the entire domain than a fixed-form test does (Northwest Evaluation Association, 2003); (3) MAP RIT scores are scaled scores which allow for more accurate comparison among students; and (4) teachers know the standards being tested but do not know the individual items— thus eliminating issues such as test familiarity and teaching to specific test items. Students took the MAP test in the fall and spring. The Fall score was used to establish a baseline metric for all participants. The Spring score was matched to the Fall score to determine student growth. MAP tests all domains of science (life, physical, earth and space), so even though teachers do not teach all of these domains each year, the test actually shows a combination of growth in domains taught and retention in any domains not taught. Regardless of whether teachers were in the Non-Participant group or one of the various participant groups, all grade level teachers taught the same core content. However, the content taught by 6th, 7th, and 8th grade teachers differed. Specifically, 6th grade teachers taught weather and climate, energy, and diversity of life; 7th grade studied matter and its interactions, living systems, heredity, and ecosystems; and 8th grade studied forces and motion, waves, Earth’s place in the universe, Earth’s system, and Earth’s history. Data Collection and Analysis MAP science scores were collected for students in all three participant groups plus the non-participant group (Non-Participants, Year 1 Participants, Prior Participants, and Year 2/3 Participants) for each year of the program. All student growth scores were computed by subtracting the Fall MAP RIT score from the Spring MAP RIT score. This growth score was then compared to the VCG score to determine the growth relative to the VCG. A positive RIT score above the VCG average indicates that the student performed above what was expected when compared to similar students whereas a negative RIT score indicated below expected performance. The VCG score was computed by matching the MAP scores of 21–51 students of non-participants from around the state to each student in the four groups of teachers (Non-Participant, Year 1 Participants, Prior Participants, and Year 2/3 Participants).

Inquiry-Based Instruction

Each of the 21–51 VCG students were matched to a student in the study according to race, gender, same Fall MAP RIT start score, same test window from fall to spring, and similar school free and reduced lunch rate. Since the individual poverty level of a student’s family was not known, we chose to match the school poverty level as a proxy for this final criterion in determining student selection. Three scores were generated for each student in each group: (1) Scientific Practices, (2) Science Concepts, and (3) Science Composite (the average of 1 and 2). Though NGSS are currently being enacted in the classrooms of many states, no well-validated assessments were available during the data collection phase—most of which preceded the actual 2012 release of The Framework for K-12 Science Education and the 2013 release of NGSS. Similar to the Common Core for State Standards for English Language Arts and for Mathematics, the assessments to measure success relative to the NGSS performance expectations lag well behind the creation of the standards (performance expectations). Specifically, guides such as Developing Assessments for the Next Generation Science Standards (National Research Council, 2014) lagged behind the Framework by several years. While it is desirable for NGSS assessments to unite the three dimensions (Scientific Practices, Cross-Cutting Concepts, and Disciplinary Core Ideas) that comprise the performance expectations, this was not possible during the data collection period. Nonetheless, in many cases, but certainly not all, MAP questions were written so that Scientific Practices and Science Content were embedded together (e.g., indicating which pulley system will lift a box with the least effort or interpreting a GIS map to determine the best location for a wind farm). Since practices and content were not always embedded, the Scientific Practices and Science Content were separated for analysis rather than unified as written by the performance expectations. Even though the ultimate goal of assessing student learning as an integrated three-dimensional system was not possible at the time of data collection, we were able to achieve the goals set out by the National Research Council (2014) of having assessments that (1) support classroom instruction, (2) monitor student learning on a broader scale, and (3) encourage and provide opportunities for students to learn science as advocated for in NGSS. Weighing the benefits of MAP against the limitations stated, we felt that the Science MAP test was the best proxy available at the time and provided an appropriate substitution to the desired NGSS assessments. Analyzing the scores in three ways (Practices, Concepts, Composite) allowed us to consider practices and concepts as separate entities as well as view them as a composite. The composite view was our best approximation of content embedded inquiry that aligns to the wording of the new more highly integrated NGSS three-dimensional performance expectations. Further, the disaggregated view of the practices and concepts also allows us to see where students tend to struggle or thrive. Three one-way ANOVAs were performed to examine whether the student growth over the VCG benchmark on the MAP test (Scientific Practices, Science Concepts, and Science Composite) is a function of the intervention year of the teacher (NonParticipant, Year 1 Participants, Prior Participants, and Year 2/3 Participants). In cases where significance was noted, post hoc tests were run to determine where significance exists between specific groups. After these analyses were run, which included calculating effect sizes of significant findings, a more pragmatic analysis was conducted to determine the magnitude of the growth of students of participants relative to the VCG.

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Specifically, the percent above the expected VCG growth was calculated. A zero percent would indicate that student growth of participants matched the 1 year of expected growth. A 100 % increase indicates double the expected growth (2 years of growth during one academic year), whereas a 100 % decrease indicates zero noted growth.

Results Figure 1 provides a contextual overview of the data analysis for the subsequent ANOVAs. Specifically, the mean RIT scores above the expected VCG MAP scores for each intervention group are shown for each of the three tests. The expected VCG score was determined for each student of a participant in the study by matching 21–51 similar students (see methods for criteria used). For Fig. 1, zero indicates no deviation from the expected growth for the various groups on each of the specific tests. Since the expected VCG score varied depending on the characteristics of students of the participants (e.g., race, start score) within a group or for a specific MAP test, the norm for comparison was set as zero deviation from the expected growth (see Table 1 for participant details by intervention year). Student Achievement Above Expected Three one-way ANOVAs were performed to examine whether the growth above the VCG threshold on the MAP test (Scientific Practices, Science Concepts, and Science Composite) is a function of the intervention year of the teacher (Non-Participant, 1st Year Participant, Past Participant, and 2nd/3rd Year Participant). Tables 2, 3, and 4 display the means and standard deviations for each year of intervention for Scientific Practices, Science Concepts, and Science Composite, respectively.

2.5 1.92

2 RIT Scores above Expected

1.64 1.5

1.38

1.29 1.08

1.02 1

0.8

0.88 Sci. Practices

0.57 0.5

Sci. Content Sci. Composite

0 -0.5 -1

-0.47 -0.65 -0.83 Non-Partic.

Yr.1 Partic. Prior Partic. Intervention Year

Yr.2 or 3 Partic.

Fig. 1 RIT score above expected vs. intervention year of students’ teachers

Inquiry-Based Instruction Table 2 Means and standard deviations for scientific practices RIT scores above the comparison group Intervention year

n

Mean

SD

Non-participant

123

−.47

2.85

Year 1 Participant

47

.57

1.66

Past Participant

17

.88

.81

Year 2 or 3 Participant

32

1.36

1.39

Total

219

.12

2.44

Scientific Practices The one-way ANOVA (see Table 5) for Scientific Practices RIT Scores above the expected VCG scores revealed a statistically significant main effect [F(3,215) = 6.73, p < .001]. The test for homogeneity of variance was significant [Levene F(3,215) = 3.37, p < .05], leading to Games-Howell post hoc comparisons being conducted to determine which of the pairings had significant mean differences. These results (see Table 6) indicate that students of teachers who were involved in Year 1, Prior Year, and Year 2/3 of the intervention had significantly higher than expected growth relative to the VCG on the MAP Science Practice test when compared to the students of Non-Participants. Though differences were noted among the growth scores above the VCG for students of teachers involved in the different intervention years (higher for each subsequent year of intervention), none was significantly different. Effect sizes where calculated for all significant findings using Cohen’s d. Effect sizes for the three significant findings ranged from .45 to .82 which falls within the medium to high threshold (Keppel & Wickens, 2004). Science Concepts The one-way ANOVA (see Table 7) for Science Concepts MAP RIT Scores above the VCG revealed a statistically significant main effect [F(3,215) = 13.48, p < .001]. The test for homogeneity of variance was not significant [Levene F(3,215) = 1.58, p > .05], leading to Tukey’s HSD post hoc comparisons being conducted to determine which of the pairings had significant mean differences. These results (see Table 8) indicate that students of teachers who were involved in Year 1, Prior Year Participant, and Year 2/3 of the intervention had significantly higher than expected growth relative to the VCG on the MAP Science Table 3 Means and standard deviations for science content RIT scores above the comparison group Intervention year

n

Mean

SD

Non-participant

123

−.83

3.18

Year 1 Participant

47

1.02

1.74

Past Participant

17

1.29

1.47

Year 2 or 3 Participant

32

1.92

1.34

Total

219

.13

2.83

J. C. Marshall et al. Table 4 Means and standard deviations for science composite RIT scores above the comparison group Intervention year

n

Mean

SD

Non-participant

123

−.65

3.02

Year 1 Participant

47

.80

1.71

Past Participant

17

1.08

1.18

Year 2 or 3 Participant

32

1.64

1.38

Total

219

.13

2.64

Concepts test when compared to students of Non-Participants. Though differences were noted among the growth scores above the VCG for students of teachers involved in the different intervention years (higher for each subsequent year of intervention), none was significantly different. Effect sizes for the three significant findings ranged from .72 to 1.13, which is considered medium to high effect sizes (Keppel & Wickens, 2004). Science Composite The one-way ANOVA (see Table 9) for Science Composite RIT Scores above the VCG revealed a statistically significant main effect [F(3,215) = 9.96, p < .001]. The test for homogeneity of variance was significant [Levene F(3,215) = 2.35, p < .01], leading to Games-Howell post hoc comparisons. These results (see Table 10) indicate that students of teachers who were involved in Year 1, Prior Year Participant, and Year 2/3 of the intervention had significantly higher than expected growth relative to the VCG on the MAP Science Composite test when compared to students of NonParticipants. Further, students of Year 2/3 Participants had higher than expected growth relative to the VCG on the MAP Science Composite test when compared to students of Year 1 Participants. Though differences were noted among the scores of students of those involved in the other intervention groups, none is significantly different. Effect sizes for the four significant findings ranged from .59 to .98, which is considered to be a medium to high effect size (Keppel & Wickens, 2004). The results indicate that for all three MAP tests (Practices, Concepts, Composite) that students of participating teachers had significantly higher than expected growth relative to the VCG when compared to students of Non-Participants, and, although not significant in all cases, the students of participating teachers had increasingly greater growth above the VCG with each additional year of participation for each MAP test. Since all of these comparisons were made relative to the expected growth established by the VCG, it is difficult to fully understand the meaning of the raw numbers by Table 5 Analysis of variance for scientific practices RIT score above comparison group Source Between

SS 111.75

Df

MS

F

P

3

37.252

6.726

.000

Within

1190.72

215

Total

1302.48

218

5.54

Inquiry-Based Instruction Table 6 Games-Howell post hoc results and effect sizes of scientific practices RIT score above comparison group by year of intervention
Mean differences X i −X j (Effect size in parentheses) Group

Mean

0

0. Non-participant

−.47

0

1

2

1. Year 1 Partic.

.57

−1.04* (.44)

0

2. Past Partic.

.82

−1.35*** (.61)

−.307

0

3. Year 2/3 Partic.

1.32

−1.83*** (.79)

−.79

−.48

3

0

*p < .05; **p < .01; ***p < .001

themselves. Therefore, Table 11 was created which converts the student scores into the percentage above what would be expected based on the predictive VCG score. For instance, students of Year 2/3 Participants, on average, had a growth that was 75.70 % above what would be expected or nearly a full year of additional growth above the expected 1 year of progress. The growth percentages above the expected VCG predictive score ranged from −23.40 % for students of Non-Participants on the MAP Scientific Practices test to a high of 82.25 % for students of Year 2/3 Participants on the MAP Science Concepts test. The following trend was consistent for each test: students of Year 2/3 Participants exceeded the VCG expectations by the greatest amount followed by students of Prior Year Participants followed by students of Year 1 Participants and then lastly were the students of Non-Participants.

Discussion This study provided a rigorous matching of each student’s score to a VCG benchmark score to determine what an expected growth rate should be for each student on each of the MAP science tests analyzed (Scientific Practices, Science Concepts, and Science Composite). By using this process, this study was able to demonstrate that when PD interventions are sustained over time and provide sufficient support to encourage transformation of instructional practice, student growth can be dramatically and significantly influenced. We hypothesized that students engaged in proficient inquiry-based instruction would grow in their understanding of scientific practices; however, this Table 7 Analysis of variance for science content RIT score above comparison group Source Between

SS 275.70

Df

MS

F

P

3

91.90

13.48

.000

Within

1465.91

215

Total

1741.61

218

6.85

J. C. Marshall et al. Table 8 Tukey post hoc results and effect sizes of science content RIT score above comparison group by year of intervention
Mean differences X i −X j (Effect size in parentheses) Group

Mean

0

0. Non-participant

−.83

0

1

2

1. Year 1 Partic.

1.02

−1.85*** (.72)

0

2. Past Partic.

1.29

−2.12*** (.86)

−.27

0

3. Year 2/3 Partic.

1.92

−2.75** (1.13)

−.89

−.62

3

0

**p < .01; ***p < .001

study provides evidence that inquiry-based instruction also promoted growth in student content knowledge. To give further credence to these findings, test questions were not known ahead of time by the researchers or the participants, therefore resulting in a more valid assessment of actual unprompted growth as opposed to using a researcherdeveloped test. This increase in student content knowledge was especially critical in our understanding of the potential effects of inquiry-based instruction on student cognitive growth. While previous studies have demonstrated student growth in scientific process skills such as analysis of data and argumentation (Johnson, 2009; Wilson et al., 2009), few have addressed direct effects on student conceptual understanding of scientific content. Reform efforts suggest that frequent implementation of constructivist approaches such as inquiry-based instruction are essential if students are to master the performance expectations stated in the NGSS that now require students to model concepts, design investigations, and use evidence to support claims (Achieve, 2013; Marshall, 2013; Windschitl & Thompson, 2006). The challenge for science education leaders becomes how to make proficient inquiry-based instruction the accepted benchmark for all K-12 science teachers. Indeed, many potential hindrances to achieving proficient inquiry need to be addressed that involve aligning belief structures (Banilower et al., 2013), addressing cultural belief differences (Tobin & McRobbie, 1996), building self-efficacy (Woolfolk, 2004), improving content knowledge and PCK (Garet et al., 2001; Shulman, 1986), and providing necessary curricular and administrative support (DuFour et al., 2004; Spillane et al., 2011). Table 9 Analysis of variance for science composite RIT score above comparison group Source Between

SS 184.22

Df

MS

F

P

3

61.41

9.96

.000

Within

1337.8

215

Total

1522.0

218

6.16

Inquiry-Based Instruction Table 10 Games-Howell post hoc results and effect sizes of science composite RIT score above comparison group by year of intervention
Mean differences X i −X j (Effect size in parentheses) Group

Mean

0

0. Non-participant

−.65

0

1

2

1. Year 1 Partic.

.80

−1.45*** (.59)

0

2. Past Partic.

1.08

−1.74*** (.75)

−.29

0

3. Year 2/3 Partic.

1.64

−2.29*** (.98)

−.84** (.65)

−.55

3

0

**p < .01; ***p < .001

Further, as long as teacher accountability is linked to high-stakes testing, assessments will further exacerbate potential barriers such as the ones listed above (Krathwohl, 2002; Zohar & Dori, 2003). Therefore, assessments are a critical piece in seeing the goals of NGSS actualized (Pruitt, 2014). If future assessments become truly aligned to the performance expectations, requiring students to demonstrate much higher-order thinking and engage in the scientific practices, then PD must be facilitated that will support teachers in seeing that these goals and performance expectations are met. In the meantime, PD leaders have to assume that future tests will align to NGSS and thus design PD interventions accordingly. The same holds true for states that are creating their own new science standards because in many cases, these states will be using NGSS or The Framework for K-12 Science Education to guide their development. Table 11 Percentage difference between the average student RIT growth score and the average VCG growth score for each participant group and for each MAP test MAP test

Concepts

Practices

Composite

Participant group

Avg student growth

Avg VCG growth

% Difference

Non-Participant

2.70

3.52

−23.40

Year 1 Participant

3.71

2.69

38.09

Prior Participant

4.10

2.80

46.43

Year 2/3 Participant

4.21

2.31

82.25

Non-Participant

2.40

2.87

−16.46

Year 1 Participant

2.57

2.00

28.61

Prior Participant

3.01

2.15

40.00

Year 2/3 Participant

3.26

1.95

67.18

Non-Participant

2.55

3.20

−20.27

Year 1 Participant

3.14

2.34

34.10

Prior Participant

3.59

2.49

44.18

Year 2/3 Participant

3.76

2.14

75.70

J. C. Marshall et al.

We have known for quite some time that short duration workshops are not effective in changing instructional practice or student achievement (Darling-Hammond et al., 2009; Supovitz & Turner, 2000); however, this study also demonstrates that the growth of students of Prior Participants was even higher than students of Year 1 Participants on all three tests. This suggests that during the first year of intervention teachers were still grappling with the change. Harris and Rooks (2010) state that student achievement can be negatively impacted when teachers engage in less than proficient inquiry instruction. Thus, it may be the case that Prior Participants, during the subsequent year, more frequently enacted proficient inquiry, which then resulted in even higher growth above the expected—a j-curve effect (Yore, Anderson & Shymansky, 2005). Moreover, Year 2/3 Participants show that the continued support received during the additional or advanced PD intervention was potentially responsible for even greater growth above what was expected.

Implications Undoubtedly many variables are potentially influential in improving student growth above expected for students in science classrooms. However, the noted medium to high effect sizes in the current study indicate that the PD intervention could be a contributing factor. Figure 1 and Table 11 provide important summaries from this study. Specifically, Fig. 1 shows that student growth rates above what would be expected based on the VCG benchmark continued to increase for each MAP test (Practices, Concepts, Composite) as the intervention years increased (Non-Participants → Year 1 Participants → Prior Participants → Year 2/3 Participants). This consistent increase (without exception) is difficult to ignore particularly when the data were based on a comparison to the similarly matched VCG. The compelling finding is that the results show that inquiry-based instruction seems to have a two-fold benefit. The first benefit, which is perhaps more intuitive, results in improved growth relative to the scientific practices, but the second benefit, improving student growth on science concepts, is less intuitive. Now that concepts and practices are united in each NGSS performance expectation, this study’s findings suggest that proficient inquiry-based instruction has the potential to improve student learning in both domains—not just scientific practices. Table 11 adds an infrequently seen component to the analysis—specifically how much greater is the growth rate from what was expected based on the established VCG benchmark? The fact that students of teachers involved in this intervention exceeded the expected growth by 29–82 % is profound. When these findings are coupled with previous findings that the achievement gap was narrowed (proficiency rates increased) for all groups measured (males, females, Caucasian, African-American, Hispanics), then it seems to suggest that proficient inquiry-based instruction is beneficial for all, or at least most, types of learners in the middle schools measured (Marshall & Alston, 2014). Indeed many other variables could be involved, such as teachers’ educational history, administrative support, and teachers’ years of experience to name a few. However, race, gender, start scores, test dates, and free-and-reduced lunch rates of the school were all controlled for in the VCG student matches. It is not known from this study whether the growth gains are more lasting than with more teacher-centered approaches, but it is possible that by providing more opportunities for students to

Inquiry-Based Instruction

engage with content before formal explanations that there will be a longer lasting effect on student learning. Future research has the potential to further explore this question. While results from this study provide evidence of positive student growth resulting from a sustained PD focused on an inquiry instructional model, there are additional challenges to inquiry implementation that extend beyond the scope of a PD experience. Teacher growth in instructional practices becomes irrelevant unless positive student outcomes are valued as part of the assessment structure. Currently, assessments intended to measure these new performance expectations have yet to be released. Specifically, these new measures will require students to demonstrate mastery with higher-level thinking skills (e.g., analyze, justify, model) and the scientific practices (Krathwohl, 2002; Zohar & Dori, 2003). Ultimately, the real drivers for learning, in high stakes environments like the current US educational system, are the assessments used to measure student achievement and/or growth. If the scientific practices that comprise inquiry-based instruction are not measured as a significant portion of the overall benchmark for success, then the instruction in most classrooms will default to only teaching content so that it directly mirrors high-stakes measures of accountability. Ultimately, multiple factors will affect student achievement in science; however, inquiry-based instruction looks to be a critical piece in the puzzle. Supporting teachers through sustained PD experiences as they develop and hone their skills in implementing effective inquiry represents a key foundational step in supporting student growth in science.

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