With increasing focus on active learning in college classrooms, many institutions of higher education are redesigning introductory laboratory classes to provide more active-learning opportunities for students and to more authentically recreate the practices of scientists. These classes are primarily taught by graduate teaching assistants (GTAs), who often lack the pedagogical training necessary to plan for and support students' intellectual engagement in rich science tasks that require deep engagement in the practices of science and the core disciplinary ideas. We believe that graduate student discussion groups can provide an opportunity to encourage and equip GTAs with pedagogical knowledge and skills to select and use cognitively demanding instructional tasks. In this article, we describe our planning and facilitation of one such meeting with a group of GTAs about the relative cognitive demands of various laboratory activities. We propose that regularly scheduled meetings of discussion groups like this can help build learning communities among GTAs. We provide strategies to support GTAs' professional development and help them think critically about the tasks they use in their classes. In particular, we highlight the importance of the cognitive demands of tasks for engaging students in active and rigorous opportunities for science learning.

Introduction

Institutions of higher education are increasingly replacing traditional classroom activities, which attempt to transmit knowledge from instructors to students, with inquiry-based activities that allow students to make observations, conduct experiments, develop and test hypotheses, and make connections that support the construction of knowledge (Keselman, 2003; Pedaste et al., 2015). Consistently, science teaching frameworks (e.g., Woodin et al., 2010) emphasize providing students with opportunities to engage in cognitively demanding tasks that integrate both scientific content (ideas, concepts, and principles) and scientific practices. For example, the Framework for K–12 Science Education identifies the following practices as essential for all students to learn (NRC, 2012):

  • Asking questions

  • Developing and using models

  • Planning and carrying out investigations

  • Analyzing and interpreting data

  • Using mathematics and computational thinking

  • Constructing explanations

  • Engaging in argument from evidence

  • Obtaining, evaluating, and communicating information

While research demonstrates that inquiry-oriented tasks are more effective than traditional tasks in promoting student learning (Hmelo et al., 1993; Engle & Conant, 2002; Hmelo-Silver, 2004; Berland & Reiser, 2009), research has also shown that successful implementation of inquiry-oriented tasks depends heavily on the instructors' teaching experience and understanding of how students learn (Van Driel et al.,1998; Barnett & Hodson, 2001; Abell, 2013). At many colleges and universities, however, instructors for introductory lab courses are graduate teaching assistants (GTAs) who may have little to no pedagogical training or experience in teaching (Travers, 1989; Sundber et al., 2005; Lampley et al., 2018).

Although many institutions provide some form of GTA training, it is often short (one to a few days) and insufficient to advance GTAs' understanding of teaching and student learning (Rushin et al., 1997; Kurdziel et al., 2003). To create durable changes in GTAs' teaching, research has suggested that training should be ongoing, discipline-specific, and sensitive to GTAs' time budgets (Williams & Roach, 1992; Young & Bippus, 2008; Gardner & Jones, 2011). Further, training should prepare GTAs to create rigorous learning environments in which students are intellectually challenged throughout the lesson (Kurdziel et al., 2003).

Here, we suggest that discussion groups (e.g., journal clubs; Honey & Baker, 2011) can provide opportunities for GTA professional development in a manner that is discipline-specific and – ideally – ongoing. Further, we outline one discussion group meeting (facilitated by the authors for a graduate student group) that more long-term discussion groups can use and extend to support the members' implementation of rigorous science tasks in their classrooms to facilitate intellectually demanding learning experiences for students. Specifically, we introduced GTAs to the Task Analysis Guide in Science (TAGS; Tekkumru-Kisa et al., 2015) with a task-sorting activity (Tekkumru-Kisa et al., 2019). This meeting was designed to help GTAs recognize the cognitive demands of science tasks and support their ability to select and implement rigorous science tasks. Reflecting on this experience, we discuss how similar meetings, regularly scheduled across the semester(s), can be supportive of GTAs' thinking about how their teaching practices can provide rigorous opportunities for students' learning.

Background – Task Analysis Guide in Science

TAGS (Figure 1; Tekkumru-Kisa et al., 2015) is a two-dimensional framework used to identify the level and kind of thinking required of students to engage in a task. The first dimension is cognitive demand, which is presented along the vertical axis of TAGS. Cognitive demand increases as tasks demand more “interpretation, flexibility, and the construction of meaning” (Tekkumru-Kisa et al., 2015, p. 5) and require some degree of ambiguity. That is, the “right” answer or preferred method is not made obvious to the students; thus, this approach engages them in deeper thinking and sensemaking (Doyle, 1983; Tekkumru-Kisa et al., 2015). The second dimension is presented in the columns, which identify whether the task focuses students' attention on science content only, scientific practices only, or their integration.

Figure 1.

The Task Analysis Guide in Science, with cognitive demand levels on the y-axis and science practices, science content, and integration of content and practices presented in the columns.

Figure 1.

The Task Analysis Guide in Science, with cognitive demand levels on the y-axis and science practices, science content, and integration of content and practices presented in the columns.

Establishing a Discussion Group

Incorporating discussions of pedagogy into existing discussion groups (often dedicated to research) is one option for GTAs seeking professional development. In our experience, members of these groups (graduate students, faculty, postdocs, etc.) either have taught or are teaching and are interested in improving their teaching. Thus, these groups are often amenable to discussion of pedagogy in their discipline. Forming a department-level discussion group dedicated to pedagogy within the department's discipline can provide more frequent and regular opportunities for GTAs' professional development. Reaching out to the department's graduate student association is a good way to find willing participants.

Research on teachers' learning and professional development suggests that effective professional development programs are spread out over time and are of sufficient duration (Hawley & Valli, 1999; Desimone, 2009; Wilson, 2013; Roth, 2015). While this literature suggests that long-term professional learning experiences are best, the total amount of contact hours required to support changes in GTAs' teaching is an empirical question for future research in a higher-education context.

Planning & Conducting a Discussion Group Meeting

Careful planning is an integral part of the design of effective professional learning programs (Borko et al., 2014; Tekkumru-Kisa & Stein, 2017). When planning and conducting our discussion group meeting, we followed the Five Practices Framework for planning and facilitating productive discussions (Stein et al., 2008; Tekkumru-Kisa & Stein, 2017), which provides suggestions and guidelines for what to attend to and be prepared for when facilitating a productive discussion with learners in a professional development context. In the discussion group meeting that we conducted with GTAs, we introduced the TAGS framework and had GTAs categorize a set of undergraduate classroom or laboratory tasks based on the framework. In planning and conducting our meeting, we followed the eight steps outlined below (those steps that are directly inspired by the Five Practices are marked with an asterisk).

Preparing for the Meeting

Select instructional tasks to analyze.

To facilitate discussion about the wide range of cognitive demand levels of science tasks, we suggest selecting a variety of tasks that fall into different categories of TAGS. You may also want to use tasks that many GTAs are familiar with, such as those found in introductory lab manuals. Print out a list of the tasks so that every participant can have one.

Anticipate ideas that are likely to arise during the discussion.*

Informed by prior research (e.g., Tekkumru-Kisa et al., 2017) and by M.T.-K.'s experiences working with secondary-school science teachers, we anticipated possible challenges that GTAs would experience as they analyzed the tasks. For example, we anticipated how GTAs would think about the distinction between science content and science practices and about the relative levels of cognitive demand on student thinking. Anticipating how participants would think helped us to plan for addressing their questions and confusions; in our introductory remarks (step 3), we displayed and discussed the scientific practices included in the bulleted list above (National Research Council, 2012) and explained the distinguishing features of cognitive demand levels.

Guiding the Task-Sorting Activity

Provide appropriate background information to motivate discussion about the relative cognitive demands of tasks.

Before our discussion, we gave a brief presentation that provided a short description of reform-based science education, TAGS, scientific practices, and cognitive demand levels.

Facilitate small-group discussions around task analysis.

We formed groups of three or four and then passed out the list of tasks (drawn from lab manuals) to be analyzed using TAGS. It is important to encourage evidence-based discussion. We asked GTAs to identify the features of each task that they used to support their classification. For example, if they said, “It is an integrated task that can be classified as Guided Integration,” we asked them to specify what features of the task led them to think that students are required to engage in scientific practices and science ideas. Encouraging evidence-based discussion is useful, not only because GTAs will require that approach from their students, but because it allows the discussion to be directed toward particular portions of the task for reference and analysis.

Monitor participants' ideas as they work in small groups for recurring themes and ideas to highlight during the whole-group discussion.*

The facilitator should walk around the small groups and ask questions to determine how GTAs are thinking about the cognitive demands of the tasks.

Facilitate a whole-group discussion on the categorization of tasks.

Proceeding one task at a time, ask participants to share how they categorized the task according to TAGS. Having monitored the small-group discussion, you can intentionally select and sequence participants' ideas/classifications to highlight contrasting perspectives and build a logical progression toward your main takeaways related to the cognitive demands of science tasks.

Wrapping Up the Discussion

Connect the discussion to the big ideas of teaching and learning.*

Support connecting participants' ideas with each other and with the major takeaways about the cognitive demands of tasks, such as (a) that different tasks provide different opportunities for students' intellectual engagement (Doyle, 1983; Blumenfeld, 1992; Hiebert & Grouws, 2007; Stein et al., 2008; Tekkumru-Kisa et al., 2015); (b) that not every task used in classrooms needs to be a high-level task – there will be times when different types of low-level tasks will be needed, but it is important to be mindful of the goals for student learning (Stein et al., 2008; Tekkumru-Kisa et al., 2015); and (c) that being aware of the intended cognitive demands of tasks is an important first step to implementing those tasks with the appropriate cognitive demands (Tekkumru-Kisa & Stein 2015).

Have a closing discussion and participant reflection.

After the group has come to agree on the categorization of each task, we suggest ending the discussion by asking the participants whether they found the discussion productive. Allow participants to reflect on the discussion by considering the ways in which it will inform their teaching.

Reflection

We observed active participation of GTAs when we facilitated our discussion group meeting. Presenting our peers with the scientific practices and TAGS provided a common language for later discussions about the cognitive demands of tasks used in lab classes. Further, the majority of GTAs found our discussion productive. While we did not empirically investigate the impact of this experience on their teaching (that was beyond the intended scope of this meeting with GTAs), many GTAs expressed that they would find it helpful to incorporate TAGS into their instructional practices.

Conclusion

Calls for science education reform reveal the need for opportunities to engage students in cognitively demanding tasks that integrate both scientific content (i.e., science ideas, concepts, and principles) and scientific practices. Although GTAs operate as scientists in their programs, it may be difficult for them to make connections between their own development as scientists and the development of their students' learning of science. Many GTAs are unequipped with the pedagogy and tools to promote science learning. This can be addressed by providing the support they need to select and implement cognitively demanding tasks that require students to intellectually engage in scientific practices and ideas.

We used TAGS as a guiding framework in our meeting. Individually, GTAs can use this framework to select tasks that better align with the practices of science and scaffold tasks with varying cognitive demands while encouraging student engagement with science practices. Moreover, meetings similar to the one described here can be used during GTA workshops, professional development seminars, graduate student orientations, or other graduate student meetings. Although a single meeting will not be sufficient to teach GTAs to select and implement cognitively demanding science tasks, an initial meeting can certainly be used to introduce this idea to GTAs. Once the idea has been seeded, future meetings can be organized to support GTAs in analyzing cognitive demands of tasks from the classes that they teach, modifying low-level tasks to make them more cognitively demanding, and discussing how to effectively implement cognitively demanding tasks. Because effective professional development should be ongoing (Williams & Roach, 1992; Young & Bippus, 2008; Gardner & Jones, 2011), we recommend that discussion group meetings dedicated to discipline-specific pedagogy meet regularly throughout the semester. These regular, structured meetings can be used as one platform to advance cognitively demanding tasks and thereby provide more rigorous opportunities for science learning in most undergraduate classes.

References

References
Abell, S.K. (
2013
). Research on science teacher knowledge. In
Handbook of Research on Science Education
(pp.
1119
1164
).
New York, NY
:
Routledge
.
Anderson, M.S. & Swazey, J. P. (
1998
).
Reflections on the graduate student experience: an overview
.
New Directions for Higher Education, no. 101
,
3
13
.
Barnett, J. & Hodson, D. (
2001
).
Pedagogical context knowledge: toward a fuller understanding of what good science teachers know
.
Science Education
,
85
,
426
453
.
Berland, L.K. & Reiser, B.J. (
2009
).
Making sense of argumentation and explanation
.
Science Education
,
93
,
26
55
.
Blumenfeld, P.C. (
1992
).
Classroom learning and motivation: clarifying and expanding goal theory
.
Journal of Educational Psychology
,
84
,
272
.
Borko, H., Jacobs, J., Seago, N. & Mangram, C. (
2014
). Facilitating video-based professional development: planning and orchestrating productive discussions. In
Transforming Mathematics Instruction
(pp.
259
281
).
Cham, Switzerland
:
Springer
.
Carroll, J.G. (
1980
).
Effects of training programs for university teaching assistants: a review of empirical research
.
Journal of Higher Education
,
51
,
167
183
.
Desimone, L.M. (
2009
).
Improving impact studies of teachers' professional development: toward better conceptualizations and measures
.
Educational Researcher
,
38
,
181
199
.
Doyle, W. (
1983
).
Academic work
.
Review of Educational Research
,
53
,
159
199
.
Engle, R.A. & Conant, F.R. (
2002
).
Guiding principles for fostering productive disciplinary engagement: explaining an emergent argument in a community of learners classroom
.
Cognition and Instruction
,
20
,
399
483
.
Gardner, G.E. & Jones, M.G. (
2011
).
pedagogical preparation of the science graduate teaching assistant: challenges and implications
.
Science Educator
,
20
(
2
),
31
41
.
Hawley, W.D. & Valli, L. (
1999
). The essentials of effective professional development: a new consensus. In L. Darling-Hammond & G. Sykes (Eds.),
Teaching as the Learning Profession: Handbook of Policy and Practice
(pp.
127
150
).
San Francisco, CA
:
Jossey-Bass
.
Hiebert, J. & Grouws, D.A. (
2007
). The effects of classroom mathematics teaching on students' learning. In F.K. Lester Jr. (Ed.),
Second Handbook of Research on Mathematics Teaching and Learning
(pp.
371
404
).
Charlotte, NC
:
Information Age
.
Hmelo, C.E., Williams, S.M., Vye, N.J., Goldman, S.R., Bransford, J.D. &
Cognition and Technology Group at Vanderbilt
(
1993
).
A longitudinal study of the effects of anchored instruction on mathematical problem solving transfer
. Paper presented at
American Research Association Meeting
,
April 12–16
,
Atlanta, GA
.
Hmelo-Silver, C.E. (
2004
).
Problem-based learning: what and how do students learn?
Educational Psychology Review
,
16
,
235
266
.
Honey, C.P. & Baker, J.A. (
2011
).
Exploring the impact of journal clubs: a systematic review
.
Nurse Education Today
,
31
,
825
831
.
Keselman, A. (
2003
).
Supporting inquiry learning by promoting normative understanding of multivariable causality
.
Journal of Research in Science Teaching
,
40
,
898
921
.
Kurdziel, J.P., Turner, J.A., Luft, J.A. & Roehrig, G.H. (
2003
).
Graduate teaching assistants and inquiry-based instruction: implications for graduate teaching assistant training
.
Journal of Chemical Education
,
80
,
1206
.
Lampley, S.A., Gardner, G.E. & Barlow, A.T. (
2018
).
Exploring pedagogical content knowledge of biology graduate teaching assistants through their participation in lesson study
.
Teaching in Higher Education
,
23
,
468
487
.
National Research Council
(
2012
).
A Framework for K–12 Science Education
.
Washington, DC
:
National Academies Press
.
Nicklow, J.W., Marikunte, S.S. & Chevalier, L.R. (
2007
).
Balancing pedagogical and professional practice skills in the training of graduate teaching assistants
.
Journal of Professional Issues in Engineering Education and Practice
,
133
,
89
93
.
Park, C. (
2004
).
The graduate teaching assistant (GTA): lessons from North American experience
.
Teaching in Higher Education
,
9
,
349
361
.
Pedaste, M., Mäeots, M., Siiman, L.A., De Jong, T., Van Riesen, S.A., Kamp, E.T., et al. (
2015
).
Phases of inquiry-based learning: definitions and the inquiry cycle
.
Educational Research Review
,
14
,
47
61
.
Prieto, L.R. & Altmaier, E.M. (
1994
).
The relationship of prior training and previous teaching experience to self-efficacy among graduate teaching assistants
.
Research in Higher Education
,
35
,
481
497
.
Puntambekar, S. & Hubscher, R. (
2005
).
Tools for scaffolding students in a complex learning environment: what have we gained and what have we missed?
Educational Psychologist
,
40
,
1
12
.
Roth, W.M. (
2015
).
The role of soci(et)al relations in a technology-rich teaching | learning setting: the case of professional development of airline pilots
.
Learning, Culture and Social Interaction
,
7
,
43
58
.
Rushin, J.W., De Saix, J., Lumsden, A., Streubel, D.P., Summers, G. & Bernson, C. (
1997
).
Graduate teaching assistant training: a basis for improvement of college biology teaching & faculty development?
American Biology Teacher
,
59
,
86
90
.
Ryker, K. & McConnell, D. (
2014
).
Can graduate teaching assistants teach inquiry-based geology labs effectively?
Journal of College Science Teaching
,
44
,
56
63
.
Schussler, E.E., Bautista, N.U., Link-Pérez, M.A., Solomon, N.G. & Steinly, B.A. (
2013
).
Instruction matters for nature of science understanding in college biology laboratories
.
BioScience
,
63
,
380
389
.
Schussler, E.E., Read, Q., Marbach-Ad, G., Miller, K. & Ferzli, M. (
2015
).
Preparing biology graduate teaching assistants for their roles as instructors: an assessment of institutional approaches
.
CBE–Life Sciences Education
,
14
,
ar31
.
Smith, M.S., Stein, M.K., Arbaugh, F., Brown, C.A. & Mossgrove, J. (
2004
). Characterizing the cognitive demands of mathematical tasks: a sorting activity. In G.W. Bright & R.N. Rubenstein (Eds.),
Professional Development Guidebook for Perspectives on the Teaching of Mathematics
(pp.
45
72
).
Reston, VA
:
NCTM
.
Stein, M.K., Engle, R.A., Smith, M.S. & Hughes, E.K. (
2008
).
Orchestrating productive mathematical discussions: five practices for helping teachers move beyond show and tell
.
Mathematical Thinking and Learning
,
10
,
313
340
.
Sundber, M.D., Armstrong, J.E. & Wischusen, E.W. (
2005
).
A reappraisal of the status of introductory biology laboratory education in U.S. colleges and universities
.
American Biology Teacher
,
67
,
525
529
.
Tekkumru-Kisa, M., Schunn, C. & Coker, R. (
2017
).
Promoting teachers' learning to select cognitively demanding science tasks
. Paper presented at the meeting of
European Science Education Research Association
,
August 21–25
,
Dublin, Ireland
.
Tekkumru-Kisa, M., Schunn, C., Stein, M.K. & Reynolds, B. (
2019
).
Change in thinking demands for students across the phases of a science task: an exploratory study
.
Research in Science Education
,
49
,
859
883
.
Tekkumru-Kisa, M. & Stein, M.K. (
2015
).
Learning to see teaching in new ways: a foundation for maintaining cognitive demand
.
American Educational Research Journal
,
52
,
105
136
.
Tekkumru-Kisa, M. & Stein, M.K. (
2017
).
A framework for planning and facilitating video-based professional development
.
International Journal of STEM Education
,
4
,
28
.
Tekkumru‐Kisa, M., Stein, M.K. & Schunn, C. (
2015
).
A framework for analyzing cognitive demand and content‐practices integration: task analysis guide in science
.
Journal of Research in Science Teaching
,
52
,
659
685
.
Travers, P.D. (
1989
).
Better training for teaching assistants
.
College Teaching
,
37
,
147
149
.
Van Driel, J.H., Verloop, N. & De Vos, W. (
1998
).
Developing science teachers' pedagogical content knowledge
.
Journal of Research in Science Teaching
,
35
,
673
695
.
Wheeler, L.B., Maeng, J.L., Chiu, J.L. & Bell, R.L. (
2017
).
Do teaching assistants matter? Investigating relationships between teaching assistants and student outcomes in undergraduate science laboratory classes
.
Journal of Research in Science Teaching
,
54
,
463
492
.
Williams, D.E. & Roach, K.D. (
1992
).
Graduate teaching assistant perceptions of training programs
.
Communication Research Reports
,
9
,
183
192
.
Wilson, S.M. (
2013
).
Professional development for science teachers
.
Science
,
340
,
310
313
.
Woodin, T., Carter, V.C. & Fletcher, L. (
2010
).
Vision and change in biology undergraduate education, a call for action – initial responses
.
CBE–Life Sciences Education
,
9
,
71
73
.
Young, S.L. & Bippus, A.M. (
2008
).
Assessment of graduate teaching assistant (GTA) training: a case study of a training program and its impact on GTAs
.
Communication Teacher
,
22
,
116
129
.