Using Grain Size Analysis as the Basis for a Research Project in an Undergraduate Sedimentology Course

Transcription

1 Using Grain Size Analysis as the Basis for a Research Project in an Undergraduate Sedimentology Course K. Siân Davies-lum Environmental Science Program, Interdisciplinary Arts and Sciences, Box , University of Washington, 1900 Commerce St, Tacoma, WA ksdavies@u.washington.edu ABSTRACT Multi-week, research style projects within the context of a one-semester undergraduate course provide scope to explore topics in depth and build student skills. I developed a research-style project for an undergraduate sedimentology course using grain size analysis of sediments that combines developing field and laboratory skills, improving writing ability, exploring statistical techniques and encouraging critical thinking. The project asks students to consider the hypothesis that the grain size distribution of sediment samples can be successfully used as fingerprints to identify sediment transport processes and depositional environments. To do this groups of students work together collecting sediment from beach environments, analyzing the grain size distribution of these samples and use statistical analyses such as histograms and log-probability graphs to help interpret their data. The use of log-probability graphs, which are a somewhat controversial technique in sediment grain size analysis, is chosen to introduce students to contentious issues in scientific research and to encourage critical thinking. Success of the project is dependent on providing students with sufficient time and feedback and to re-write reports, analyze data and contribute to classroom presentations and discussions. Development of such projects requires thoughtful selection of a topic that will meet desired pedagogical objectives and careful structure so that time is managed effectively. INTRODUCTION There are many pedagogical benefits to introducing research-style projects within the confines of a semester-long undergraduate course (Carlson, 1999); these include the potential for in-depth explorations of a topic and the opportunity to gain experience of the research process. I developed a multi-week, research-style project for a small, upper level sedimentology class in which students conduct grain size analysis on beach sediment and use histograms and log probability graphs to analyze and interpret grain size distributions of sediment collected from beach environments. The project had four pedagogical objectives: (1) To give students experience in field and laboratory techniques, (2) To introduce students to statistical concepts in sedimentology, (3) To facilitate critical thinking and questioning of scientific literature and (4) To improve students' scientific writing. Three iterations of this project have enabled me to consider whether these pedagogical objectives were being met and develop the project accordingly. Here I present some of the sedimentological theory behind the project, discuss project implementation, consider the drawbacks and benefits of the project and provide some suggestions for how similar projects may be developed. GRAIN SIZE ANALYSIS IN SEDIMENTOLOGY Sedimentology courses commonly include a section on petrology that focuses on the composition, characteristics and origin of sedimentary rocks. Rock texture is a crucial concept in sedimentary petrology, integrating observations of sediment grain size, shape and distribution. Textural analysis is used in a variety of ways in the interpretation of sediment transport systems and depositional histories (Friedman 1967, Passega 1957.) For example, siliciclastic sedimentary rocks with high proportions of large-sized sediment grains, such as sand or gravel, are inferred to have been deposited in higher energy environments than those with high proportions of small-sized grains, such as clay. Sediment grain size distributions have not only been used to interpret modes of sediment transport and environments of deposition but they have also been used to categorize and classify siliciclastic rocks (Folk 1951). In the simplest case the proportions of sand, silt and clay in a rock determine whether the rock is defined a sandstone, siltstone or claystone. Sediment grain size can readily be studied quantitatively in a laboratory setting and a variety of techniques, including histograms, bivariate plots and probability graphs, have been used to analyze the grain size distribution of sediment populations. Histograms that plot grain size classes on the x-axis and the proportion of grains in each grain size class on the y-axis (Figure 1) can be used to glean general information about the grain size distribution of the sediment population in individual samples. The most abundant grain size class (mode) of the sample can be discerned from the peak on the histogram whereas sorting in the sample is generally expressed by the spread of data along the x-axis. Information about the sorting of a sample is particularly useful when interpreting grain size distributions because it may indicate transport processes; for example aeolian sands tend to show very well sorted, uni-modal grain size populations. Skewness and kurtosis of a sediment population have been used as indicators of sorting; they can be observed qualitatively on a histogram of grain size distribution but are also derived arithmetically. Skewness compares the sorting in the coarser and finer grained halves of a sediment sample. In normal sediment distributions the mean, medium and mode of the population coincide but in skewed distributions they do not. For example in negative skewed distributions coarse grains are less well sorted than fine grains. Kurtosis or peakedness compares the sorting in the central portion of the grain size distribution with sorting in the tails (ends) of the distribution; it is less commonly used in grain size interpretations than skewness. A wide variety of bivariate plots, in which two different grain size parameters are plotted against each other, have also been used in grain size analysis and interpretation with varying success (Friedman 1967, Tanner 1991.) Log probability plots or log-phi graphs (Figure 2) plotted on probability paper have also 10 Journal of Geoscience Education, v. 54, n. 1, January, 2006, p

2 Figure 1. Histograms for (a) winter berm and (b) swash-backswash environments along a single transect sampled by one student group. The grain size distribution for the winter berm has a mode at 2 phi (medium to fine sand), it is poorly sorted with a positive skewness resulting from excess particles in the 4-9 phi (silt-clay) range of grain size categories. The grain size distribution for the swash-back swash sample has a mode at 2 phi (medium to fine sand), it is better sorted than the winter berm sample with all grains in the 0-3 phi range (all sand size grain categories). commonly been used in sediment grain size analyses (Sengupta et al. 1991). Numerous papers record the development of this technique (Inman 1949, Moss 1963, Spencer, 1963) culminating in the summary paper by Visher (1969). Visher's paper (1969) describes how the distribution of grains in a siliciclastic rock or unconsolidated sediment sample may be related to their transport processes and environments of deposition by using log-probability plots and it is often cited in textbook discussions of grain size analysis (e.g., Prothero and Schwab 2004, Boggs 1995). The interpretation of such plots relies on the assumption that in nature the grain size distribution of a sediment population is composed of a mixture of several log normal distributions and thus will appear as a series of segmented curves when cumulative frequency is plotted against log-normal grain size, commonly known as phi units. Phi units are a logarithmic transformation of the Udden-Wentworth grade scale (the standard scale used to classify sediment grain sizes) in which the negative logarithm to the base 2 Figure 2. Example of a log-probability graph adapted from Visher (1969) for a sediment sample from the lower swash zone that has been interpreted as having four log normal populations:the traction, saltation and suspended sediment populations. of the grain diameter (in millimeters) is substituted for the diameter value (Bates and Jackson, 1984). The segments on log-probability plots have commonly been described as a coarse and fine tail together with a central segment (Tanner 1991); they have been interpreted as indicating different sediment transport processes and have been used as "fingerprints" for recognizing depositional environments in ancient sedimentary rocks (Visher 1969, Greenwood 1972, Leeder 1982). For example, Visher (1969) recognized distinctive slope breaks marking segments in log-probability plots of sediment from a variety of depositional environments including Holocene beach sands from South Carolina. He related the slope breaks in the log-probability plots of the beach sand to the traction load, two saltation loads (swash and back swash) and a suspended load (Figure 2); a pattern which he also claimed to find in ancient beach sandstones (Visher 1999). Log-probability plots can be of particular importance for interpreting environments of formation for sediment and sedimentary rocks extracted in cores, where small of sample sizes and lack of large-scale sedimentary features make environmental interpretations difficult. The use of sediment log-probability curves to identify sediment transport processes and environments Davies-lum - Using Grain Size Analysis 11

3 has been criticized. Some researchers have challenged the assumption that the straight line or line segments on log-probability curves are due to sediment samples composed of either a single or a mixture of log-normal sediment distributions e.g. Christiansen et al. (1984) and Sen Gupta et al. (1991). Even for sediment samples that do commonly have log-normal distributions, such as well-sorted dune sands, it is not always easy to interpret the line segments of a log-probability plot because these may be truncated or overlapping (Tanner 1991) and n-1 line segments may be defined on the graph from n size determinations. More in depth discussions of the criticisms and benefits of using log-probability curves can be found in Sinclair (1976) and Syvitski (1991). PROJECT IMPLEMENTATION Integration into a Sedimentology Course - The project is conducted as part of an upper level sedimentology course that is taken by geology and environmental earth science majors who have usually had at least three other geology courses (an Introductory Geology course, Mineralogy and Earth History.) Most students take the class in either their junior or senior year and many have had exposure to some kind of geologic research experience. The course is taught over a 15 week semester, with classes meeting for lecture twice a week and lab once a week. The project and associated classes account for approximately 4 class sessions, 2 lab sessions and 1 field trip conducted during class/lab time. Other class sessions that meet during the project concentrate on related topics such as the petrology of siliciclastic rocks and sediment transport processes. Class size is small at between 6 to 10 students, which facilitates research groups of 3 or 4 students that are formed at random. The project accounts for 25 of the course grade with the remaining 75 coming from more traditional lab and field assignments and a final exam. Student Preparation - Students are introduced to the concepts of grain size analysis and techniques for analyzing grain size through class lectures, textbook readings and examination of samples and equipment. Time in class is spent ensuring that students understand the use of the phi system of grain size classification, which is used to create log-probability plots. Although most students in this class have had previous exposure to statistics prior to taking the sedimentology course, statistical concepts of particular relevance to grain size analysis are covered in class; discussion focuses on how the central tendencies (mean, mode and median), and skewness and kurtosis of grain size distributions (Folk, 1959) have been interpreted by sedimentologists. The construction of histograms is reviewed and probability plots are introduced as a way of presenting and interpreting grain size data. In cases where students do not have any statistical background more time would have to be spent covering the statistical concepts used in this project. Students are assigned to read the 1969 paper by Visher, which summarizes the use of log-probability plots, and are required to answer questions about the paper. These included simple content-related questions about the hypotheses and methodologies presented in the paper as well as more conceptual questions regarding the environmental interpretations of log-probability graphs and their use in sedimentology (Visher 1969). Students generally provided comprehensive answers for questions that required simple descriptions; answers to questions about data interpretation and application of the technique were not as complete. Student responses to questions form the basis of an in-class discussion on the ideas and techniques presented in the paper during which students are encouraged to share and compare their answers. Emphasis is placed on the sections of the paper that focus on log probability plots and transport processes in beach environments to prepare students for collecting and analyzing sediments from these environments. That discussion is used as a springboard to the next part of the project: preparation for assessing whether logprobability graphs can be used to identify transport processes and beach environments of a local beach. Students are randomly assigned to groups and provided with information about the beach from which they will collect samples. Each group is given time to consider their strategies for collecting beach sand samples then groups come together to discuss their ideas. Most groups decide that collecting sand from along transects perpendicular to the coastline is the most effective sampling strategy and is a convenient way to record and identify beach sub-environments, which is important for considering the analyses described in Visher (1969). Groups that favor a more random sampling strategy are encouraged to ensure that they collect samples from all beach environments so that they can plot the full range of log-probability plots for beaches that are given in Visher (1969.) Field Work and Sample Collection - Sediment collecting is conducted at a local beach. Sampling equipment is simple: tape measures, clinometers (to measure changes in beach slope), zip-lock bags, markers and trowels. Collectively, students identify the swash zone, berms and back beach environments of the beach and take samples from them following the sampling strategy that they have chosen. After collection, the samples are dried which is the only preparation required before grain size analysis. LABORATORY ANALYSIS Each group conducts grain size analysis of their beach samples using a Coulter LS200 laser particle size analyzer. This equipment analyzes samples quickly, enabling all groups to analyze their samples within a day or so, and it has good reproducibility. Students check the reproducibility of their samples by analyzing each at least twice and then comparing the resultant grain size distributions. Unlike more conventional techniques for grain size analysis, the laser particle size analyzer gives sediment data as a volume percent rather than as a weight percent and grain sizes are given in micrometers, which are converted to phi units to create log probability graphs. Conveniently, this equipment can be configured to automatically produce histograms of grain size distribution and to calculate central tendencies of grain size distributions for each sample. Students use this feature to automatically produce the histograms and to compute central tendencies. They are required to manipulate their results to draw probability graphs by hand and to understand their implications in order to discuss sediment sorting and transport processes in the laboratory report. Discussion of the benefits and drawbacks of the use of laser particle size analyzers in 12 Journal of Geoscience Education, v. 54, n. 1, January, 2006, p

4 Figure 3. Log-probability graph for the winter berm environment corresponding to histogram shown in figure 1(a). In this interpretation of the graph three line segments have been identified: line segment A from 0 phi to 2 phi, line segment B from 2 phi to 4 phi and line segment C from 4 phi to 8 phi. Note that students produced the plot by hand; this version is a representation of that plot created in a graphics program. grain size analysis is outside the scope of this paper but can be found in Loizeau et al. (1994) and McCave et al. (1986.) If this equipment is not available, grain size analysis could be conducted using more traditional sieve methods at no detriment to the project. Data Analysis - During class time each group reviews their grain size data and histograms and creates a cumulative probability curve for each of their samples. Probability curves are created by hand; there is no simple program that allows them to be constructed by computer. The graphs in figures 2 and 3 are reproductions of log-probability curves, originally produced by hand by students, which were constructed using a graphics program. Groups attempt initial interpretations of the probability curves, assessing whether they can identify line segments and if any identified line segments can be related to transport processes in the beach environments that they sampled. Each group is required to present their results formally then the groups convene to compare their findings and to discuss what these might mean in terms of Visher's assertions about environmental interpretations of log probability curves (Visher 1969). This discussion is also the forum for which the pros and cons of the technique, described elsewhere in this article, can be addressed. Assignments and Grading - Three individual written reports are required from students (Table 1). Individual rather than group reports are assigned to ensure that each student is able to articulate the research that they are conducting. An initial report is assigned after the field trip that requires each student to research and describe the local geology and geomorphology of their collecting locality, outline their sampling methodology and state the goals of their project. A second report is assigned following laboratory analyses; it requires an account of lab methodologies, a description and analysis of the data and an interpretation of sediment sorting in the samples analyzed. At this time each group turns in the histograms for their samples for checking. Both reports are returned promptly with feedback. After the final classroom discussion, the project culminates with a full final report that requires an introduction and background to the project, field and lab methodologies, an interpretation of results, conclusions, figures and references. Grading rubrics, as described by Tewksbury (1996), were introduced during the third iteration of the project. The rubrics used described requirements and expectations for each assignment on a scale of 1-5, with numbers corresponding to letter grades. Table 2 shows Davies-lum - Using Grain Size Analysis 13

5 Timing Activity Groups Hand In Individuals Hand In Preparation 1. Lecture on grain size classification and analysis 2. Textbook reading assignment: grain size analysis Week One Week Two Week Three 1. Visher (1969) paper assignment 2. Discuss paper in class 3. Collection of field samples 1. Grain size analysis 2. Produce histograms Introductory write up returned with comments 1. Groups work on probability curves in class 2.Class Discussion of pros and cons of methods used. Lab write up returned with comments Nothing Histogram End of Project Final report due Probability Curves 1. Answers to questions on Visher (1969) paper 2. Introductory write up includes: (a) Geology and geomorphology of sampling location (b) Statement of Problem (c) Sampling strategy and rationale Lab write up includes: (a) Laboratory methodology (b) Interpretation of histograms: sorting, relationship to beach environment Final write up includes: (a) Background geology (b) Methods (c) Results (d) Synthesis of data, consideration of Visher s hypothesis (e) Conclusions Table 1. Schedule of student activities and assignments. the rubric that was used for the students' field report, the first report that they produce. Each of the 3 reports was graded using a rubric specific to that assignment and returned with feedback for improvement. Rubrics were found to significantly improve the overall quality of reports, especially greater attention to detail and more thoughtful and carefully edited writing. The final report for the project requires a combination of rewriting work from previous assignments and new writing on the interpretation of probability curves and conclusions following class analysis and discussion. Allowing students the opportunity to re-write work based on feedback resulted in clear improvement in their writing. Revision also allows students to re-assess their interpretations in the light of further analysis and data collection. The final report counts twice as much towards the final grade for the project as the preceding reports. This is significant as it gives the students the incentive to significantly raise their overall grade for the project by working on their writing and critical thinking skills, in addition to giving credit to students who acted on the feedback given to them in previous assignments. Sample Results - Table three shows the data that one student group collected for a single beach transect. The volume percent of grain size from 9 phi to 0 phi are shown for each sample in addition to the mean, median and mode in micrometers. Histograms are given to illustrate the grain size distributions for two samples; one collected from the winter berm at 1 meter along a transect (Figure 1a) and the other from the swash-back swash zone at 44 meters along a transect (Figure 1b). The grain size distribution for the winter berm has a mode at 2 phi (medium to fine sand) and is poorly sorted with a positive skewness resulting from excess particles in the 4-9 phi (silt-clay) range of grain size categories. The grain size distribution for the swash back-swash sample has a mode at 2 phi (medium to fine sand) and is better sorted than the winter berm sample with all grains in the 0-3 phi range (all sand size grain categories.) A log probability graph for the winter berm (Figure 3) is also given. It has been interpreted as having three line segments, line segment A from 0 phi to 2 phi, line segment B from 2 phi to 4 phi and line segment C from 4 phi to 8 phi. According to Visher (1969) these line segments may be interpreted as a traction population (segment A), a saltation population (segment B) and a suspended population (segment C.) Students have some success in identifying line segments on their plots however most students conclude that based on their data, log-probability plots of grain size distributions are not a reliable way of identifying sediment transport modes in beach environments. Their conclusions are based on comparing their results for beach environments to those presented by Visher (1969, 1999) and from considering the variation in results obtained between groups. Students interpretations of 14 Journal of Geoscience Education, v. 54, n. 1, January, 2006, p

6 Grade Criteria Approximate Grade 5 Outstanding; excellent field notes and sketches; sophisticated introduction with excellent, referenced supporting information; superior understanding of Visher paper shown; field methodology well thought out and described; excellent, clear, concise writing throughout assignment. A Goes beyond an adequate job; very good field notes and sketches; strong introduction with good, referenced, supporting information; very good understanding of Visher paper shown; field methodology well thought out and described; good, clear, concise writing throughout assignment. Does not have the originality required for a 5. Average, solid job; adequate field notes and sketches; solid introduction but lacks referenced supporting information; adequate understanding of Visher paper shown; field methodology adequately thought out and described; writing solid but needs work on clarity and style; in short a solid job that just does what the assignment asks. Below average; poor field notes and sketches; introduction does not have enough detail and has no supporting references; poor understanding of Visher paper shown; field methodology poorly thought out and inadequately described; poor writing that needs work on most aspects; coverage is cursory and does not meet the minimum required for a complete assignment. 1 Poor; minimal/no field notes and sketches; introduction is cursory with no supporting references; expresses a muddled understanding of Visher paper; field methodology as for 2 but also muddled; very poor writing that needs work on all aspects; coverage is F less than cursory and shows major flaws in reasoning and understanding. 0 No work handed in 0 B C D Table 2. Grading rubric used for the first report that students turn in. This report includes information on field locality and sample collection. In some instances, where the work falls between the categories indicated I will give half grades e.g. 4.5 log-probability graphs would be made easier if subdivisions of phi units were used to give larger grain size data sets and this will be incorporated in future versions of the project. PROJECT ASSESSMENT AND IMPROVEMENT Drawbacks and Benefits - Although the use of log-probability graphs to interpret sediment grain size distribution has been criticized it is important to note that there really is no generally accepted method for the interpretation of sediment transport processes in depositional environments. Sedimentologists have yet to find the textural key that really allows them to link grain size distribution, transport and depositional setting and a discussion on the use of log-probability curves in sediment textural analysis remains a component in Sedimentology textbooks. Log-probability graphs can provide an interesting way to consider the connection between sediment grain sizes, transport processes and environments of deposition if they are used with an appreciation for their complexity and the criticisms that they have generated. The use of probability graphs in analyzing sediment grain size distributions can be deceptively simple and it is crucial that students are made aware of their drawbacks. Sufficient time must be given for students to discuss their interpretations, for groups to share their findings and for the instructor to guide students through some of the criticism that this technique has generated. However, using such a contentious technique to analyze data can be incorporated as a positive learning experience because it introduces students to controversial issues in scientific research and encourages them to be questioning of published ideas and data. Student Assessment and Feedback - It was not possible to assess whether students taking the sedimentology course significantly improved their analytical and writing skills through the introduction of this project because it was implemented the first time that I taught the course and there were no base line evaluations from previous courses. However, evaluation forms were used to assess student perceptions about the project and what they had learned from it. Table 4 shows the average values from responses provided by 14 students over two iterations of the project. All students agreed that their knowledge and experience of sedimentary transport systems, laboratory and statistical techniques had improved and appreciated that the multi-week project allowed them to analyze a subject in more depth than a single lab assignment would allow. However, most felt that they would only want to do an extended project of this type once during a semester and were uncertain whether such projects gave them greater experience of the scientific method. In general, students felt that the project was well paced with reasonable deadlines and they all valued the opportunity to re-write material as part of the final report. Working in groups frustrated some students, even though write-ups and grading were individual in an attempt to overcome such frustrations. Overall students felt that the project was a valuable experience. Suggestions for Further Development - The pros and cons of the technique could be further explored by asking students to analyze samples from a variety of environments to see if each has a characteristic plot as Visher (1969, 1999) suggests. It would also be possible to analyze mixed samples to see if plots that are supposed to be specific to a particular environment of deposition are produced from a sample that contains sediment from a variety of environments. Studying the composition of the sediment sampled could be an addition to this project either using X-Ray diffractometry, to investigate the general mineralogy of sediment samples or a Franz isodynamic-separator, to Davies-lum - Using Grain Size Analysis 15

7 Position on transect 0 m 1 m 5 m 10 m 14 m 20 m 44 m 49 m Env Winter Berm Winter Berm Summer Berm Summer Berm Edge of swash zone Swash backswash zone Swash backswash zone Swash backswash zone Mean grain size mm Median grain size mm 9 phi 8 phi 7 phi 6 phi 5 phi 4 phi 3 phi 2 phi 1 phi 0 phi Mode grain size mm Table 3. Grain size data for beach sediment samples collected by one student group along a single beach transect. The winter berm at 0m marks the start of the transect. The histograms shown in Figure 1 are for the winter berm at 1 meter and the swash-backswash zone at 44 meters along the transect. The log probability graph in Figure 3 is for the winter berm at 1 meter. Question Average Response Did the project increase your understanding of sedimentary transport systems? 3.9 Did the project increase your experience of sedimenaty lab procedures? 4.5 Did the project broaden your experience of using statistical techniques in geology? 3.1 Did the project increase your experience of using the scientific method? 2.7 Table 4. Summary of student evaluations of the project. Students were asked to evaluate on a scale of 1-5, where 1=no, not at all and 5= yes, very much so. These values average the response given by 14 students over two iterations of the project. study the ferromagnesian component of sediments. The influence of provenance as expressed by compositional variation could also be addressed by subdividing samples compositionally, for example by extracting ferromagnesian grains, and analyzing them for grain size before and after subdivision. I have experimented with developing a compositional "add-on" to the grain size project but have found that the greater amount of time required for this is detrimental to student engagement and that the additional content detracts from the focus on sediment textural studies. IMPLEMENTING MULTI-WEEK RESEARCH-STYLE PROJECTS Despite increasing examples in the literature of the benefits of research style projects in undergraduate courses (Carlson 1999), geoscience students are still commonly assigned small-scale, weekly laboratory assignments. A project of this type gives students the experience of working on a scientific question from data collection through to critical analysis and interpretation. In addition to giving students hands-on experience in field and laboratory techniques and developing writing skills through encouraging re-writes, this project also exposes them to real issues encountered in research such as the use of a controversial technique and the challenges of working in a group. These are all valuable skills and experiences for students intending to continue in academic research and those planning a career in geosciences. This project was developed specifically for a sedimentology class but multi-week research style projects could be introduced into many other mid- and upper- level geoscience courses. Creating such projects requires careful selection of topic and consideration of objectives. Whilst all projects can be used to address student writing and critical thinking skills I chose to focus on a particular aspect of sedimentology that would give students field and laboratory experience as well as practice in statistical analysis. Selection of the objectives for and content of a project also needs to take time management into account. It is important that the project 16 Journal of Geoscience Education, v. 54, n. 1, January, 2006, p

8 is long enough to enable in-depth analysis but short enough to maintain student enthusiasm and focus; three weeks is probably the optimum length of time for such projects. Multi-week projects also require careful planning and it is important to give students a detailed schedule with a clear articulation of goals for each step of the project, in addition to rubrics for assignments, so that they know exactly what is required of them. The general issues that faculty face in implementing this kind of active-learning project over traditional laboratory and lecture teaching, are summarized in Harris (2001). The main drawback of a multi-week project such as this is that it is time consuming and ultimately requires some sacrifice in course content. However, it has been documented that a greater depth of understanding and competence more than makes up for a loss in breadth of content in the chosen subject matter (Tewksbury, 1997). Thoughtful selection of a project that attempts to meet clearly articulated pedagogical objectives within a time frame of a few weeks is key to success. CONCLUSIONS Working on a multi-week research type project requires greater intellectual investment and engagement from a student than traditional laboratory assignments conducted during a single laboratory class and allows more in depth exploration of a topic. If such a project is thoughtfully constructed and the topic carefully selected it can also provide opportunities for students to gain experience in field, laboratory and analytical techniques as well as improve their scientific writing. Incorporating a questionable analytical technique into research type projects, such as the log-probability curves used in this project, can be a beneficial educational experience by introducing students to controversies in the scientific literature and encouraging the development of critical thinking skills. ACKNOWLEDGEMENTS I would like to thank the Pomona sedimentology students who participated in this project, Don Zenger for support and encouragement and a number of reviewers whose comments greatly improved the manuscript. A permit from California State Department of Parks and Recreation enabled collection of beach sediment and I am grateful to state ecologist David Pryor for supporting my application for this permit. REFERENCES Bates, R.L. and Jackson, J.A., 1984, Dictionary of Geological Terms, New York, Anchor Books, 571 p. Boggs, S., Jr., 1995, Principles of Sedimentology and Stratigraphy, New Jersey, Prentice Hall, 774 p. Carlson, C.A., 1999, Field research as a pedagogical tool for learning hydrogeochemistry and scientific writing skills, Journal of Geoscience Education, v. 47, p Christiansen, C., Blaesild, P., and Dalsgaard, K., 1984, Re-interpreting 'segmented' grain-size distributions, Geological Magazine v. 121, p Folk, R.L., 1951, Stages of textural maturity in sedimentary rocks, Journal of Sedimentary Petrology, v. 21, p Folk, R.L., 1959, Petrology of Sedimentary Rocks, Austin, Hemphill's, 154 p. Friedman, G.M., 1967, Dynamic processes and statistical parameter compared for size frequency distribution of beach river sands, Journal of Sedimentary Petrology, v. 37, p Greenwood, B., 1972, Modern analogues and evaluation of a Pleistocene sedimentary sequence, Transactions of the Institute of British Geographers, v. 56, p Harris, M.T., 2001, Strategies for implementing pedagogical changes by faculty at a research university, Journal Geoscience Education v. 49. p Inman, D.L., 1949, Sorting of sediment in light of fluvial mechanics, Journal of Sedimentary Petrology v. 19, p Leeder, M.R., 1982, Sedimentology: Process and Product, Allen and Unwin, London, 344p. Loizeau, J.-L., Arbouille, D., Santiago, S. and Vernet, J.-P., 1994, Evaluation of a wide range laser diffraction grain size analyzer for use with sediments, Sedimentology v. 41, p McCave, I.N., Bryant, R.J., Cook, H.F. and Coughanowr, C.A., 1986, Evaluation of a laser-diffraction analyzer for use with natural sediments, Journal of Sedimentary Petrology v. 56, p Moss, A.J., 1963, The physical nature of common sandy and pebbly deposits, part II, American Journal of Science v. 261, p Passega, R., 1957, Texture as a characteristic of clastic deposition, American Association of Petroleum Geologists Bulletin, v. 41, p Prothero, D.R. and Schwab, F., 2004, Sedimentary Geology: An Introduction to Sedimentary Rocks and Stratigraphy (2nd Edition), New York, W, H, Freeman and Co., 557 p. Sen Gupta, S., Ghosh, J.K., and Mazumder, B.S., 1991, Experimental-theoretical approach to interpretation of grain size frequency distributions, in Syvitski, J.P.M., editor, Principles, Methods and Application of Particle Size Analysis, Cambridge, Cambridge University Press, 368 p. Sinclair, A.J., 1976, Applications of probability graphs in mineral exploration, Special ume 4 of the Association of Exploration Geochemists, Richmond, Richmond Printers, 95p. Spencer, D.W., 1963, The interpretation of grain size distribution curves of clastic sediments, Journal of Sedimentary Petrology v. 33, p Syvitski, J.P.M., 1991, Principles, Methods and Application of Particle Size Analysis, Cambridge, Cambridge University Press, 368 p. Tanner, W.F., 1991, Suite Statistics: The hydrodynamic evolution of the sediment pool p in Syvitski, J.P.M., editor, Principles, Methods and Application of Particle Size Analysis, Cambridge, Cambridge University Press, 368 p. Tewksbury, B.J., 1997, Innovative and Effective Teaching in the Geosciences: National Association of Geoscience Teachers publication, 87 p. Tewksbury, Barbara J, 1996, Teaching without exams - the challenges and benefits, Journal of Geoscience Education, v. 44, p Visher, G.S., 1999, Stratigraphic Systems: origin and Application, 3rd edition, Academic Press, San Diego, 700 p. Visher, G.S., 1969, Grain size distribution and depositional processes, Journal of Sedimentary Petrology, v. 39, p Davies-lum - Using Grain Size Analysis 17

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