Thompsonetal2016_ReenvisioningtheIntroductoryLabasCognitiveApprenticeship

84 Journal of College Science Teaching

RESEARCH AND TEACHING

Introductory science courses play a critical role in the recruitment and retention of undergraduate science majors. In particular, first-year courses are opportunities to engage students in scientific practices and motivate them to consider scientific careers. We developed an introductory course using a semester-long series of established laboratory experiments closely aligned with lecture topics that allow students to participate in a cognitive apprenticeship. In this course, students learn basic concepts in cell and molecular biology during lecture and apply their content knowledge and acquire research skills in the laboratory during a series of related experiments. The ongoing theme and course structure enable students to critically analyze their results each week as they would in a research laboratory. Assessment results show that students gain an understanding of research and laboratory techniques and demonstrate evidence of knowledge transfer from the course to related scientific journal articles. Students also learn content equivalent to a more general molecular biology course. Centering a course on a semester-long laboratory project can provide a solid foundation of content knowledge and an authentic introduction to scientific research.

Reenvisioning the Introductory Science Course as a Cognitive ApprenticeshipBy Meredith M. Thompson, Lucia Pastorino, Star Lee, and Paul Lipton

Introductory undergraduate sci-ence courses are a critical junc-ture for students considering STEM (science, technology, en-

gineering, and mathematics) majors and have been the focal point for science education reform (Brewer & Smith, 2011). Ideally, the laboratory aspect of introductory science cours-es gives students an opportunity to learn and practice important skills, reinforce knowledge of lecture con-cepts, and understand scientific in-vestigation (Hofman & Lunetta, 2002); however, many introductory undergraduate laboratory classes are a series of “cookbook laboratories” that are not well integrated into the course (Handlesman et al., 2004). As first-year science courses are gate-ways for potential STEM majors, it is useful to consider a few key ques-tions. How can we design first-year courses to show connections be-tween the fundamental content cov-ered in lecture and the process cov-ered in the laboratory? How can we structure these courses so students in them learn and practice important specific laboratory techniques and also develop broader scientific think-ing skills?

Conceptual frameworkInduction into the scientific commu-nity shares many characteristics of an apprenticeship (Sadler, Burgin, McKinney, & Ponjuan, 2010). This apprenticeship engages participants in authentic activities and practices

in the domain (Lave & Wenger, 1991). A cognitive apprenticeship is a “learning-through-guided expe-rience on cognitive and metacogni-tive, rather than physical, skills and processes” (Collins, Brown, & New-man, 1989, as cited in Dennen & Burner, 2008, p. 427). Students not only learn what to do, they learn how to think about what they are doing by having ongoing feedback from more experienced instructors. Students are given multiple opportunities to engage with the material they are learning in a context-rich environ-ment (Hendricks, 2001). Cognitive apprenticeships begin by modeling how an expert would approach the material. Students receive coaching in the form of feedback and advice, and the instructor provides scaf-folding to help students learn, asks students to articulate their learning process, and encourages students to reflect on what they have learned. As students gain understanding of the topic, they are given less structure and more opportunities to explore and apply their knowledge on their own (Collins, Brown, & Holum, 1991; Dennen & Burner, 2008). We designed this course with these ideas in mind, incorporating the modeling, coaching, articulation, reflection, and application of their understand-ings during the course. The course is both an actual apprenticeship, in that students practice laboratory skills and processes, and a cognitive ap-prenticeship where students learn

85Vol. 46, No. 1, 2016

how to think metacognitively about the larger context of the experiment.

Course descriptionDesigned for potential neuroscience majors, Introduction to Cellular and Molecular Biology (abbreviated NE for its focus on neuroscience) covers such fundamental concepts as DNA replication; translating genes into pro-teins; and how the cell regulates key processes such as mitosis, membrane synthesis, and transport. The lecture component of NE covers the standard complement of cellular and molecu-lar biology topics seen in the majority of introductory biology courses (Ta-ble 1). The syllabus is available in the supplemental materials (http://www.nsta.org/college/connections.aspx).

Learning skills in context is an im-portant aspect of apprenticeships. In this course, the skills and experiments in the laboratory are nested within the context of the series of laboratory

experiments. Lecture and laboratory are thoroughly intertwined, and lab ex-ercises are a direct application of each week’s lecture content. The seamless integration of lab and lecture has been linked to student learning of content and process, and student motivation (Burrowes & Nazario, 2008). Adding the cognitive apprenticeship approach provides a framework that helps instructors create positive learning environments for students. Students are able to learn laboratory skills from experts by watching them model those skills and to learn approaches to sci-entific experimentation that are often implicit but are made explicit as part of the cognitive apprenticeship model. Instructors are encouraged to match the difficulty of the material being covered through scaffolding and to provide opportunities for students to articulate what they have learned and to reflect on their own learning. Each of these concepts is described briefly below.

ModelingIn modeling, an expert practitioner demonstrates the skills or tech-niques to be learned (Collins et al., 1991). The laboratory component is designed to mirror a research lab setting focused on a semester-long investigation of the mechanisms of Alzheimer’s disease. Students read original journal articles and conduct a series of published ex-periments (Pastorino, Ikin, Nairn, Pursnani, & Buxbaum, 2002). This long-term experiment aligns with our learning goals of exposing stu-dents to techniques and aspects of scientific research by motivating them to apply information learned in the lectures much as students do in problem based learning (PBL) frameworks (Eberlein et al., 2008; Pease & Kuhn, 2011; Sungur & Tekkaya, 2006). Our course adds to the rich context of PBL with a fo-cus on modeling scientists’ thought

TABLE 1

Overview of introductory neuroscience course.Week Theme Lecture Lab

1 DNA DNA structure

DNA replication and repair

Regulation of gene expression

Recombinant DNA technology

Transcription

Students use polymerase chain reaction (PCR) to replicate BACE DNA.

Students use recombinant DNA techniques by inserting the BACE DNA into a plasmid.

2

3

4

5

6 Protein Protein structure and function

From DNA to protein: transcription and translation

Students insert BACE DNA into cells and use SDS-PAGE and Western Blot to test levels of protein expression.

7

8 Protein function and transport in the cell

Membrane structure

Transport across the cell

Intracellular compartments

Protein transport

Students label wild type and mutant BACE with specific antibodies to track BACE movement using a fluorescence microscope.

Students focus on studying the processing of Amyloid Precursor Protein APP, as a result of BACE activity on APP, as it occurs in Alzheimer’s disease pathology.

9

10

11



processes. The laboratory methods parallel those discussed in the ar-ticle as depicted in Table 1.

CoachingIn a cognitive apprenticeship, a ma-jor part of the coaching occurs when the instructors make their thinking explicit, or “visible,” to the students (Bareiss & Radley, 2010). At the beginning of each week, a lecture is dedicated to covering the week’s experiment to prepare and enable students to work independently. The professor models how to read and interpret journal articles, in-cluding the importance of reading figure legends and understanding the logical flow of the article during the weekly class session. Students are challenged to connect lecture material with the experimental con-cepts that are emphasized in lab, where they and graduate teaching fellows (TFs) actively engage in discussions about technical details and rationale. This convergence of topics between the two parts of the course allowed the lab instructor and professor to support students in developing critical thinking skills and in transferring ideas from one context into a different context. A major challenge of this course was to remind students that the purpose of research is to answer the overall research question, as students often believed the purpose of the labora-tory was to learn specific labora-tory techniques. To ensure that stu-dents understood why they needed to clone DNA, graduate TFs and undergraduate learning assistants (LAs) revisited this topic repeatedly as students gained more informa-tion in lecture. On a weekly basis, graduate TFs were responsible for continuously reviewing each as-pect of the research project with the

students. Students had multiple op-portunities to understand and revisit the topics from week to week, and the lab instructors had multiple op-portunities to identify and address areas that were difficult for students to understand.

Weekly staff meetings included discussions of pedagogical strate-gies, such as how to engage students in active learning, and metacogni-tive strategies, such as how to help students interpret journal articles and troubleshoot anomalous results (Dotger, 2010).

ScaffoldingInstructors structure learning envi-ronments by matching the level of difficulty to students’ abilities, a process known as scaffolding. By focusing on challenging yet attain-able goals, scaffolding reduces the cognitive load on the learner (Brans-ford, Brown, & Cocking, 2000). In this course, the close connection between the lecture and the labora-tory allowed the course instructors to support student learning. The lecture met three times a week: two of those lectures provided an over-view of the foundational concepts and the third lecture was dedicated to exploring the specific content that would be covered in the labo-ratory. This third lecture included a preview of the laboratory and time to review and discuss experimental results; it also emphasized connec-tions between that week’s experi-ment and the larger context covered in lecture.

Prior to coming to the labora-tory session, students completed an online prelab quiz. Quiz questions emphasized the critical components and reasoning for the upcoming experiment. At the start of each lab, graduate TFs and undergraduate LAs

led a class discussion reviewing the quiz and linking the purpose of the current experiment with the overall research project.

Articulation and reflectionStudents demonstrate understand-ing through articulating their knowl-edge in discussions and assignments and through reflection by compar-ing their own approach with that of solving problems with an expert (Collins et al., 1991). Because stu-dents were following an established protocol, they were encouraged to compare their results with those of their classmates and consider fac-tors that may have caused their own experiment to deviate from the ex-pected results. Failed experiments were viewed as learning opportu-nities to deepen the analysis of the causes and consequences of the ex-periments.

The assignments in the course also required students to articulate and reflect on their learning in the course. Students read a review article about molecular mechanisms involved in Alzheimer’s disease as a first assign-ment and then more detailed journal articles as their second and third as-signments. These assignments were specific to the field of the research project, and functioned to (a) in-troduce the rationale and technical aspects of the project and (b) help the students understand how data are communicated in a research article with an emphasis on the different aspects of a research article.

A cumulative final assignment was focused on writing the lab proj-ect in the format of a research article, where students formulated their own scientific argument and logical pre-sentation of their data. In this assign-ment, students described the goals of the project, the techniques used, and

87Vol. 46, No. 1, 2016

the results in a format that included the (a) Summary (or Abstract) where they summarized the objective of the experiment and the results obtained; (b) Introduction, where students reviewed preliminary information necessary to understand the project; (c) Materials and Methods, where students described the methods used; and (d) Results and Discussion, where students had to report and explain their data and also emphasize their relevance in the context of that field of research.

Exploration and applicationExploration and application chal-lenge students to translate their understanding into a new context (Collins et al., 1991). In this course, students were asked to apply scien-tific thinking skills gained through the course into a new, yet related domain through a figure-ordering assignment. Students assembled a set of randomly sorted figures from a research article into a logical order and provided a rationale for their chosen sequence. The ordering of the figures demonstrated students’ level of understanding of the logi-cal thread of the scientific article, as well as the ability to use information from the figure descriptions in their ordering. Our grading rubric, in-cluded in the supplemental materials (http://www.nsta.org/college/con-nections.aspx), accounts for more than one possible order based on the evidence included in the figures. This exercise was extremely chal-lenging for the students, providing a learning opportunity for them and for the instructors in the course.

Assessment of learning outcomesWe wanted to know whether struc-turing the course with a thematic

link between the lecture and lab with the attributes of a cognitive ap-prenticeship would provide similar content coverage compared with a traditional course, help students transfer knowledge to different contexts, and help students’ under-standings of research in molecular biology. A pre- and postcourse as-sessment included content ques-tions, self-report about students’ goals for the course, and learning gains as a result of the course. Con-tent questions were also adminis-tered to a comparison group of stu-dents in a more traditional biology course with a similar range of top-ics. The figure-ordering assignment demonstrated students’ understand-ing of the structure of a scientific ar-gument and their ability to transfer knowledge from the laboratory and lecture to an assignment focused on a tangentially related scientific arti-cle. All of these survey instruments were reviewed and approved by the Boston University Institutional Re-view Board.

Content knowledge The pre- and postcourse assessment content questions were collected from existing materials and previ-ous tests (Shi et al., 2010; Smith et al., 2005) and past content questions developed by the authors for other

courses. A comparison group of stu-dents from a different introductory biology course (BI) also completed the pre- and postcourse assessment for the content questions. Labora-tory technique questions were de-veloped by members of the research team and were only completed by the students in NE.

The content knowledge questions were scored 0 for incorrect and 1 for correct responses and were averaged together by individual to create a content pretest and posttest mean. An average score for each pretest and posttest was calculated by averaging the number of correct responses on the pre- and the posttest, and these were again averaged to create a grade pretest mean and posttest mean. The normalized gain was used to com-pare scores across different classes (Singer, Nielsen, & Schweingruber, 2012). The average normalized gain for each class was calculated using the following formula:

Normalized = (average post - average pre)gain (1– average pre)

The results for the content questions are presented in Table 2. Students in biology exhibited a normalized gain of 20%, whereas students in neu-roscience had a normalized gain of 21%. The effect size of 1.29 for BI suggests that the mean of the post-

TABLE 2

Average pretest and posttest score and normalized gain for BI and NE. BI (N = 289) NE (N = 78) BI (SD) NE (SD)

Average pretest score 0.44 0.40 0.13 0.14

Average posttest score 0.55 0.52 0.04 0.13

Normalized gain 0.20 0.21

Effect size 1.29 0.94

Note: BI = introductory biology course; NE = Introduction to Cellular and Molecular Biology.



test is at the 88th percentile of the pretest. The effect size of 0.94 for NE suggests that the mean of the posttest was at the 89th percentile of the pretest. Both are considered to be large effect sizes. Overall, students in both NE and BI made similar con-tent gains related to the questions on the assessments.

Laboratory skills and techniquesStudents in NE answered a set of questions related to laboratory skills on the pre- and postcourse assess-ment. We asked students when they would use four essential laboratory techniques (colorimetric assay, trans-formation, polymerase chain reac-tion (PCR), and gel electrophoresis) and when they would use two assays (green fluorescent protein [GFP] and immunoblot assays). The aver-

age pre- and postscores as well as the results of the test of significance are presented in Table 3. The full questions and response categories are listed in the supplemental mate-rials (http://www.nsta.org/college/ connections.aspx).

NE students made significant gains in their understanding of all of the laboratory techniques as a result of the course. Not surprisingly, students made the greatest gains in the questions directly related to course content: identifying proteins, protein localization, and using immunoblots.

Evidence of transferWe measured students’ ability to transfer knowledge using the figure-ordering assignment described pre-viously. During Week 8 of class, we asked students to complete a version

of the assignment to establish a base-line. Students organized and provid-ed a rationale for the figures of the research article “BACE is Degraded Via the Lysosomal Pathway” (Koh, Von Arnim, Hyman, Tanzi, & Tesco, 2005). We gave the postassignment as a bonus question during the final exam at the end of the course. In this case, students had to reorganize only part of the figures from the article “Intracellular Itinerary of Internal-ized β-Secretase, BACE1, and its Po-tential Impact on β-Amyloid Peptide Biogenesis” (Chia et al., 2013).

Both the preassignment and post-assignments were graded by the professor and teaching fellows. Each assignment was graded by at least two people, and students could score up to 10 points for each assignment: 4 points for the correct order and 6 points for a correct rationale. The overall results of the assignment ap-pear in Table 4, and the full assign-ment description is available in the supplemental materials (http://www.nsta.org/college/connections.aspx).

Students made statistically signifi-cant gains in their ability to provide a rationale for the ordering of their figures. However, students did not improve on the figure-ordering prob-lem given in the exam compared with the in-class assignment. The total score increased from the baseline to the exam but did not reach statistical significance. The improvement in providing a rationale demonstrates an ability to critically analyze pieces of evidence into an argument and provides evidence of students’ ability to transfer the knowledge gained from NE into a different context.

Understanding of researchStudents were asked to report on their understanding of research on

TABLE 3

Lab skills average pretest and posttest scores, standard deviations, and significance values.

Lab skills M pre (SD)

M post (SD)

p-value (McNemar)

You’ve selected a 1000μL micropipette to add a volume of 420μL to your tube. The numbers on the micropipette window should read:

0.71 (0.46)

0.95 (0.22)

.001

Which technique would you choose to intro-duce proteins into cells?

0.71 (0.46)

0.94 (0.23)

.001

Which technique would you choose toseparate DNA or proteins by size?

0.31 (0.47)

0.85 (0.36)

.001

Which technique would you choose toidentify which proteins are present in the sample?

0.3 (0.32)

0.89 (0.32)

.001

Which technique would you choose todetermine where proteins localize in cells?

0.30 (0.46)

0.68 (0.47)

.002

The method scientists use to “tag” GFP to a protein of interest is

0.36 (0.47)

0.68 (0.47)

.001

An immunoblot assay uses the following biological molecule

0.55 (0.50)

0.99 (0.11)

.001

89Vol. 46, No. 1, 2016

the final survey. Eighty-five percent of students (77) reported that they better understood research as a re-sult of the course, 14% of students (13) reported they had the same understanding as before taking the course, and only 1% of students (1) disagreed with the statement that they learned more about research from the course. The course experi-ence influenced 37% of students (33) to move toward careers in research, 35% of students (27) to move away from a research career, and 30% of students (31) were not notably af-fected by the course.

The course allowed students to gain insight into different aspects of scientific research, including a better understanding of methods, protocols, and results and an ability to read and decode scientific articles. After the course, students better understood the importance of replicating experi-ments, sharing results, and the pro-cess of communicating results to the scientific community through journal articles. Students also wrote that the course demystified the process of scientific experimentation, allowing them to “better understand different scientific techniques,” inspiring them to be “more excited and interested in doing research.” In particular, the students’ comments on the postsur-vey demonstrated an appreciation of learning from failure. One student wrote, “I have gained a lot of respect for those who develop their experi-ments and experience trial and error and still continue with research.” Another commented, “I have more respect for researchers because there is so much that can go wrong.”

Students were asked how much their experience in NE influenced their interest in doing future research. Forty-four students mentioned that

their experience in the course was a positive influence. Students’ com-ments demonstrated an appreciation for the course structure with a closely related thematic lab, in the context of Alzheimer’s disease.

“I never had a class where we dedicated an entire semester to a research project and I liked it. I was able to understand better the concepts then just doing an entire different lab every week. It opened my eyes to the world of research and I am excited to start this summer.”

“[This course] makes it possible for me to consider a career in research rather than pursuing the premed path. I loved learning about Alzheimer’s disease and reaching a point where I could truly understand scientific writ-ing.”

Students’ comments specifically mention how the multiweek examina-tion in the context of a neurological disease was beneficial in their learn-ing. Students participated in a broad range of scientific practices includ-ing critically analyzing experiments, learning how to read and decipher scientific articles, and applying con-tent knowledge in the practical realm of the laboratory.

DiscussionReform in undergraduate science laboratories has centered on bringing research activities to students early in their undergraduate careers, in par-ticular on having students engage in scientific research in the form of ex-perimentation (Elgin, 2007; Hanauer et al., 2006; Wei & Woodin, 2011). Although designing and conducting experiments is important in science, it is only one part of what scientists do (Zimmerman, 2007). This article demonstrates how crafting an intro-ductory course around a theme of a series of laboratory experiments al-lows for a well-integrated course that can provide students with founda-tional knowledge equivalent to tradi-tional introductory courses and build students’ scientific thinking skills and practices.

In addition to providing students with content knowledge, this course structure is advantageous to traditional courses because it allows students to gain important insights into the scientific process and develop criti-cal thinking skills. The alignment of lecture and laboratory encouraged students to think about connections between course content and experi-mental rationale for the laboratory. Students evaluated and assessed their own work in the lab by comparing their results with the published results and also with their peers. When students

TABLE 4

Results from the figure-ordering (transfer) assignment.

Prescore Postscore p-value N Pre (SD) Post (SD)

Figure order 3.28 2.81 .037 74 1.30 1.55

Rationale 3.28 4.03 .028 74 2.39 1.98

Total score 6.15 6.77 .181 79 3.37 2.98



did not obtain the same results as in the article, the faculty and laboratory instructors used this difference as an opportunity to troubleshoot laboratory techniques and discuss the challenges of replication studies in science re-search. This format enabled students to learn about the research process through active engagement and criti-cal reflection as part of a supportive community. Students also learned how to evaluate scientific arguments by observing experts (professors and graduate TFs) and by practicing critical reflection during discussions in class and laboratory. The thematic approach revisited the material over a series of weeks, allowing students many opportunities to understand challenging material and allowing the lab instructors to identify and address areas of difficulty. In many ways, this laboratory course mirrors the appren-ticeship that a new graduate student undertakes when he or she first enters a graduate lab with the added advan-tage of having instructors model their thinking processes in addition to their physical skills.

This type of course structure has other advantages. Our class size was around 100 (only 80% consented to be included in the study), but such a course structure could be expanded to larger classes. Following an estab-lished protocol enables the students to pinpoint potential mistakes. It also enables the graduate TFs to assess student progress against a standard and provides a common topic to be discussed during lecture. The prospect of managing multiple and different research projects can create chaos for instructors and frustration for students (Crandall, 1997), and concerns about course management are a barrier for those considering course reform (Brown, Abell, Demir, & Schmidt,

2006). This structured approach pro-vides an alternative for instructors to consider as they design introductory courses.

The limitations of this approach are that the laboratory experiment cannot cover all of the topics regu-larly covered in the lecture, leaving more content for upper level courses. However, the guidelines in Vision and Change (Brewer & Smith, 2011) suggest a focus on concepts rather than facts and the unification of learning about content and process. Although introductory courses can provide students with a great deal of foundational content knowledge, modeling and stimulating critical thinking on important topics can provide them with tools they can use throughout their academic and personal lives.

ConclusionThe structure of NE provides first-year students with an introduction to scientific research processes as well as a solid foundation in the fundamental content and skills in the field of cell and molecular neurobiology. The semester-long experiment provides an ongoing theme for the course and enables students to reflect and apply their understanding of course-related ma-terial from each part of the course. While learning content equivalent to a traditionally structured course, students were actively engaged in the experiment, understanding the rationale for each step and critically evaluating their results against their peers and the published article. Such a course structure allowed students to apply knowledge they gained in multiple contexts, helped them draw on that knowledge, and allowed them to assess their own learning. ■

AcknowledgmentsThe authors acknowledge Kathryn Spilios, Elizabeth Co, and Matthew McIntyre for sharing content questions. We also thank Eli Shobin and Nathaniel Kinsky for their assistance scoring students’ responses to the figure-drawing questions. This work was supported by an award to Boston University from the Howard Hughes Medical Institute through their Undergraduate Science Education Program.

ReferencesBareiss, R., & Radley, M. (2010).

Coaching via cognitive apprenticeship. In G. Lewandowski, S. Wolfman, T. J. Cortina, and E. L. Walker (Chairs), Proceedings of the 41st ACM technical symposium on computer science education (pp. 162–166). New York, NY: Association for Computing Machinery.

Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.

Brewer, C. A., & Smith, D. (Eds.). (2011). Vision and change in undergraduate biology education: A call to action. Washington, DC: American Association for the Advancement of Science. Retrieved from http://visionandchange.org/finalreport/

Brown, P. L., Abell, S. K., Demir, A., & Schmidt, F. J. (2006). College science teachers’ views of classroom inquiry. Science Education, 90, 784–802.

Burrowes, P., & Nazario, G. (2008). Promoting student learning through the integration of lab and lecture: The seamless biology curriculum. Journal of College Science

91Vol. 46, No. 1, 2016

Teaching, 37(4), 18–23.Chia, P. Z. C., Toh, W. H., Sharples,

R., Gasnereau, I., Hill, A. F., & Gleeson, P. A. (2013). Intracellular itinerary of internalised β-secretase, BACE1, and its potential impact on β-amyloid peptide biogenesis. Traffic, 14, 997–1013.

Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator, 15(3), 6–11.

Crandall, G. D. (1997). Old wine into new bottles: How traditional lab exercises can be converted into investigative ones. Journal of College Science Teaching, 26(6), 413–418.

Dennen, V. P., & Burner, K. J. (2008). The cognitive apprenticeship model in educational practice. In J. M. Spector, M. D. Merrill, J. Van Merrienboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (pp. 425–439). Mahwah, NJ: Erlbaum.

Dotger, S. (2010). Offering more than “Here is the textbook”: Teaching assistants’ perspectives on introductory science courses. Journal of College Science Teaching, 39(3), 71–76.

Eberlein, T., Kampmeier, J. A., Minderhout, V., Moog, R., Platt, T., Varma-Nelson P., & White H. (2008). Pedagogies of engagement in science: A comparison of PBL POGIL and PLTL. Biochemistry and Molecular Biology Education, 36, 262–273.

Elgin, S. C. R. (2007). Genomics for all. HHMI Bulletin 20(3), 40–41.

Hanauer, D. I., Jacobs-Sera, D., Pedulla, M. L., Cresawn, S. G., Hendrix, R. W., & Hatfull, G. F. (2006). Teaching scientific inquiry. Science 314(5807), 1880–1881.

Handlesman, J., Ebert-May, D., Beichner, R., Bruns, P., Chang, A., DeHaan, R., . . . Wood, W. B. (2004). Scientific teaching. Science, 304(5670), 521–522.

Hendricks, C. C. (2001). Teaching causal reasoning through cognitive apprenticeship: What are results from situated learning? The Journal of Educational Research, 94, 302–311.

Hofman, A., & Lunetta, V. N. (2002). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88, 28–54.

Koh, Y. H., Von Arnim, C. A., Hyman, B. T., Tanzi, R. E., & Tesco, G. (2005). BACE is degraded via the lysosomal pathway. Journal of Biological Chemistry, 280(37), 32499–32504.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, England: Cambridge University Press.

Pastorino, L., Ikin, A. F., Nairn, A. C., Pursnani, A., & Buxbaum, J. D. (2002). The carboxyl-terminus of BACE contains a sorting signal that regulates BACE trafficking but not the formation of total A(beta). Molecular and Cellular Neuroscience, 19, 175–185.

Pease, M. A., & Kuhn, D. (2011). Experimental analysis of the effective components of problem-based learning. Science Education, 95, 57–86.

Sadler, T., Burgin, S., McKinney, L., & Ponjuan, L. (2010). Learning science through research apprenticeships: A critical review of the literature. Journal of Research in Science Teaching, 47, 236–256.

Shi, J., Wood, W. B., Martin, J. M., Guild, N. A., Vicens Q., & Knight J. K. (2010). A diagnostic assessment

for introductory molecular and cell biology. CBE—Life Sciences Education, 9, 453–461.

Singer, S. R., Nielsen, N. R., & Schweingruber, H. A. (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering. Washington, DC: National Academies Press.

Smith, A. C., Stewart, R., Shields, P., Hayes-Klosteridis, J., Robinson P., & Yuan, R. (2005). Introductory biology courses: A framework to support active learning in large enrollment introductory science courses. Cell Biology Education, 4, 143–156.

Sungur, S., & Tekkaya, C. (2006). Effects of problem-based learning and traditional instruction on self-regulated learning. The Journal of Educational Research, 99, 307–317.

Wei, C. A., & Woodin, T. (2011). Undergraduate research experiences in biology: Alternatives to the apprenticeship model. CBE–Life Sciences Education, 10, 123–131.

Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27, 172–223.

Meredith M. Thompson ([email protected]) is a research scientist in the Teach-ing Systems Lab, Department of Urban Studies, at the Massachusetts Institute of Technology in Cambridge, Massachu-setts; Lucia Pastorino ([email protected]) is a lecturer in the Department of Neuro-science at Boston University in Boston, Massachusetts; Star Lee is an academic coordinator in the Department of Biology at the University of California in Riverside; and Paul Lipton is director of the Under-graduate Program in Neuroscience at Bos-ton University.

Documents

Thompsonetal2016_ReenvisioningtheIntroductoryLabasCognitiveApprenticeship