Teaching Undergraduate
Analytical Science
with the
Process Model
The process model helps students acquire problem-solving, communication, and teamwork skills.
he formal teaching of analytical chemistry or analytical science as an accepted and respected discipline is relatively new compared to other branches of chemistry. Elements of the subject have always been taught (e.g., classical techniques), often as part of the inorganic and physical course material, but rarely by academics who would consider themselves analytical special-
T
ists. However, since the 1950s, which heralded the instrumental revolution of research and industrial applications, the discipline has achieved sufficient breadth and depth to emerge as a separate subject. At that time, several respected texts were available on analytical chemistry in the United Kingdom (1–3). However, few were suitable as a textbook to support a special-
Brian W. Woodget UK Analytical Partnership
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Table 1. Skill sets for the analytical scientist. Skill set
Associated skills related to technical approach skills
of the book coincided with the introduction of master’s programs in analytical chemistry Knowledge In-depth chemical knowledge, general knowledge of cognate scientific at U.K. universities. This textand technological disciplines, awareness of emerging analytical technologies, detailed knowledge of analytical science book soon became recommended to support these Practical skills Manipulation, observation, recording; use of generic laboratory courses. Now, of course, many instruments texts cover to varying degrees Technical approach skills Able to define objectives, select calibration method, use principles of exthe broad range of subjects enperimental design, assess safety risks; aware of need for valid analytical compassed in the wide field of measurement analytical science. Associated skils related to Able to interpret graphic and instrument data, estimate quantitative data Since analytical science beinterpretative skills and assess significance; able to use data handling techniques (chemocame a discipline in its own metrics, statistics) and make realistic decisions right, much discussion has ensued about what skills academBusiness skills Laboratory and project management; strategic, financial, and customer awareness ics should strive to instill in their students (5). In the 1960s and 1970s, numerous papers emphasized that what analysts do is not simply carry out qualist subject discipline or course module. One of the early influ- itative and quantitative analysis, but solve problems; therefore, anential student textbooks was Instrumental Methods of Analysis alytical science courses should somehow reflect this industrially by Willard, Merritt, and Dean, published in 1965 (4). Publication important attribute (6–10). Key skills and competencies
Personal, communication, numeracy, information technology, interpersonal, information gathering
Table 2. Skills required in problem-solving activities. Strategy component
Skill
Example
Identifying the problem
Communications skills
Discussion with client and/or other scientists
Interpersonal skills
Problems are rarely solved by a single person
Information gathering
Search the scientific literature before beginning a project
Observation
Site visit can be beneficial to see the problem firsthand
Representing the problem
Ability to define objectives
Why is the analysis necessary?
Selecting a strategy to solve the problem
Knowledge skills
Broad scientific knowledge required
Able to select analytical methods
Broad awareness of analytical technology
Able to design experiments
Most useful when developing solutions to routine problems
Able to design sampling plan
Consider the sampling, storage, and sample preparation
Able to develop and validate methods
New method may be needed; validate existing method for new matrix
Able to prepare samples for analysis
Choose appropriate sample preparation
Implementing the strategy
Evaluating the solutions
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Able to select calibration methods
To produce results that are fit for purpose
Practical skills
Use of laboratory equipment and instruments
Able to estimate
May save time in the long run
Able to interpret data
Likely to have data from a number of sources
Able to apply statistical analysis
Estimate uncertainty for quantitative data
Able to make realistic decisions
Do the conclusions satisfy the objectives?
Able to communicate results
Results must be communicated with explanations and significance
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It seems that 40 years later, we are still saying the same thing. Little appears to have been achieved universally in helping students acquire problem-solving and other industrially desirable skills such as teamwork and communication. Given that analytical science is a problem-solving science, we have a unique opportunity to carefully design curricula that enable undergraduate students to acquire these skills as a natural part of their learning process. In 1999, the Skills Network of the newly created UK Analytical Partnership (www.ukap.org), in extensive consultation with academia and industry, compiled a list of skills considered important for the 21st-century analytical scientist (Table 1). Although Table 1 does not specifically mention problem-solving skills, all of the skills necessary to undertake this task are encompassed within these skill sets. So where and how do analytical scientists acquire these skills and become expert problem-solvers? It would be most unreasonable to expect all new graduates to obtain all of these skills by the time they complete their undergraduate degrees. Thus, some of the higher-level skills (e.g., technical approach and business skills) will come from their continuing professional development.
The process model As far as tackling and solving real-world analytical science problems, it is, of course, necessary to have specific technical skills in addition to problem-solving skills. The generic approach to developing problem-solving skills is to learn how to identify the problem, represent it, formulate a strategy to solve the problem, implement it, and evaluate the solution (11). Given that this approach may be applied across a whole range of industries and commerce, we need to identify what specific skills are necessary in the context of analytical science. Table 2 lists the skills required to undertake each element of the problem-solving strategy and gives real-world examples. It is also pertinent to note the similarity between this generic strategy for problem solving and a model that is sometimes used to define the analytical process. Defining analysis by using a process model is not a new idea (12–15). However, research shows few cases in which the model has been adopted as an aid to teaching the subject (16). Table 3 shows the units that comprise the analytical process and the similarity between the process model and the elements of the generic strategy for problem solving. The model may be used both as a template for presenting analytical science in a coherent and logical manner and as a way to emphasize the problemsolving aspects of the discipline. It is particularly useful when introducing the analytical approach to students majoring in scientific disciplines other than chemistry. Table 4 suggests how the process units can be used to teach analytical science. This template offers teachers the flexibility to personalize their approach to match a student body profile and achieve the stated objectives of an individual course. Those with research interests in a particular area of spectroscopy, for example, may wish to concentrate on this technology at the expense of
others, yet still offer their students a broad picture of analytical science. For too long, many analytical courses have been “vertical” in their approach, and as a result, students become experts too early in narrow areas of the science (e.g., chromatography or individual aspects of spectroscopy). This scheme offers a more “horizontal” approach to the subject, giving students a broader picture and the ability to compare methodologies and technologies and make informed decisions. When instructors apply the template in both the theoretical and the laboratory parts of the course and use well-developed case studies, students will begin to see themselves as problem-
Table 3. Comparison between the units in the analytical process model and the generic strategy for problem solving. Process unit
Generic strategy
Consider the problem and decide on objectives; choose procedures to achieve objectives
Identify the problem Represent the problem Select strategy
Sampling
Implement strategy
Sample preparation Separation and/or concentration Measurement of analytes Evaluate data—have the objectives been met?
Evaluate solutions
solvers. Laboratory experiments used to support the theoretical part of the course should therefore be designed to allow students to define the objective(s) of the analysis, choose an appropriate analytical procedure, perform a simple sampling regime to learn how to select a representative sample for analysis, prepare the sample, qualitatively evaluate the sample if necessary, estimate the likely concentration level of target analyte(s) if quantitation is required, prepare calibration standards, set up and calibrate equipment, dilute prepared sample if necessary and perform the analysis, calculate and interpret the results and estimate the error, decide if the objectives have been achieved, and present the report. In addition, it is important that, when possible, students be given the opportunity to work in teams to enhance many of their skills and possibly begin developing leadership potential. All too often students work in teams for the convenience of the teachers rather than to benefit the students, thereby losing some of the opportunities to develop and experience team dynamics. J U LY 1 , 2 0 0 3 / A N A LY T I C A L C H E M I S T R Y
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Table 4. Unit topics for the curriculum. Unit 1: Consider the problem and decide on objectives Analyses cost money and are thus performed for a purpose Agree on the objectives with the client or sample provider Design analytical systems to improve quality of the product and reduce costs
Well-developed case studies or histories are used extensively in law, psychology, and medicine as an accepted part of the curriculum. More use needs to be made of case studies in teaching analytical science because they are an excellent vehicle for acquiring many important skills, particularly those associated with problem solving.
Unit 3: Sampling
Brian W. Woodget is a part-time consultant and a skills network facilitator for the UK Analytical Partnership. His research interests include teaching analytical science and developing Web-based teaching resources. Address correspondence about this article to him at UK Analytical Partnership, LGC, Queens Road, Teddington, TW11 0LY, United Kingdom (
[email protected]).
Importance of representative sampling
References
Introduce sampling terminology
(1)
Unit 2: Choose procedure to achieve objectives Choice should focus on expected level(s) of analyte, precision and accuracy required, number of samples, sample matrix, interferences, and instrument availability Method development and validation
How to sub-sample solid and liquid matrices while maintaining sample integrity
(2)
Simple and difficult sampling situations Statistical sampling Maintain sample integrity from sampling location to laboratory Unit 4: Sample preparation
(3) (4)
Need for sample preparation Sample preparation techniques
(5)
Unit 5: Separation and/or concentration Why separation and concentration are necessary and why they may be combined Introduce precipitation, solvent extraction, chromatography, and electrophoresis Unit 6: Measuring analytes In-depth or across-the-board approach to analytical techniques Comparative, relative, absolute, destructive vs non-destructive, invasive vs non-invasive
(6) (7) (8) (9) (10) (11)
Classical techniques, GC, LC, molecular and atomic spectroscopy, MS, bioassay Calibration methods and units of measure Unit 7: Evaluate data (have objectives been met?) Statistical analysis
(12) (13) (14)
Measurement uncertainty Evaluate data and report results
(15)
Decide if objectives have been met Quality assurance and accreditation Influence of regulations
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(16)
Vogel, A. I. Textbook of Quantitative Inorganic Analysis, 2nd ed.; Longman: London, 1951. Kolthoff, I. M., Elving, P. J., Eds. Treatise on Analytical Chemistry, Part 1; Interscience: New York, 1959. Wilson, C. L.; Wilson, D. W. Comprehensive Analytical Chemistry, Vol. 1A; Elsevier: London, 1959. Willard, H. H.; Merritt, L. L., Jr.; Dean, J. A. Instrumental Methods of Analysis, 4th ed.; Van Nostrand: New York, 1965. Kratochvil, B.; Harris, W. E. In Treatise on Analytical Chemistry, 2nd ed.; Part 1, Vol. 1; Kolthoff, I. M., Elving, P. J., Eds.; Interscience: New York, 1978; Section A, Chapter 2. Laitinen, H. Anal. Chem. 1970, 42, 1121. Laitinen, H. Anal. Chem. 1966, 38, 1441. Lucchesi, C. Anal. Chem. 1974, 46, 451 A. Hume, D. N. Anal. Chem. 1963, 35, 29 A. Siggia, S. Survey of Analytical Chemistry; McGrawHill: New York, 1968; Chapter 1, pp 1–8. Crawford, K.; Heaton, A. Problem Solving in Analytical Chemistry; Royal Society of Chemistry: London, 1998. Pardue, H. L.; Woo, J. CHEMTECH 1985, 15, 183– 185. Lucchesi, C. International Laboratory 1980, 68–72. Tyson, J. F. Analytical Proceedings 1989, 26, 251– 254. Settle, F., Ed. Handbook of Instrumental Techniques for Analytical Chemistry; Prentice Hall: New York, 1997; Chapter 1. Baiulescu, G. E.; Patroescu, C.; Chalmers, R. A. Education and Teaching in Analytical Chemistry ; Ellis Horwood: Chichester, U.K., 1982; Chapter 3, pp 83–86.
