Problems and Problem Solving in Chemistry Education

Analysing Data, Looking for Patterns and Making Deductions

Georgios Tsaparlis

• Publisher: Royal Society of Chemistry
• Year: 2021
• Print ISBN: 9781839162183
• eBook ISBN: 9781839163593
• Edition: 1
• Topic: Education
• Subtopic: Professional Development

Problem solving is central to the teaching and learning of chemistry at secondary, tertiary and post-tertiary levels of education, opening to students and professional chemists alike a whole new world for analysing data, looking for patterns and making deductions. As an important higher-order thinking skill, problem solving also constitutes a major research field in science education. Relevant education research is an ongoing process, with recent developments occurring not only in the area of quantitative/computational problems, but also in qualitative problem solving.

The following situations are considered, some general, others with a focus on specific areas of chemistry: quantitative problems, qualitative reasoning, metacognition and resource activation, deconstructing the problem-solving process, an overview of the working memory hypothesis, reasoning with the electron-pushing formalism, scaffolding organic synthesis skills, spectroscopy for structural characterization in organic chemistry, enzyme kinetics, problem solving in the academic chemistry laboratory, chemistry problem-solving in context, team-based/active learning, technology for molecular representations, IR spectra simulation, and computational quantum chemistry tools. The book concludes with methodological and epistemological issues in problem solving research and other perspectives in problem solving in chemistry.

With a foreword by George Bodner.

Chapter 1

Introduction−The Many Types and Kinds of Chemistry Problems

Georgios Tsaparlis^a

^a University of Ioannina, Department of Chemistry, Ioannina, Greece Email: [email protected]

1.1 Problems and Problem Solving

According to the ancient Greeks, “The beginning of education is the study of names”, meaning the “examination of terminology”.¹ The word “problem” (in Greek: «πρόβλημα»/“problēma”) derives from the Greek verb “proballein” (“pro+ballein”), meaning “to throw forward” (cf. ballistic and ballistics), and also “to suggest”, “to argue” etc. Hence, the initial meaning of a “problēma” was “something that stands out”, from which various other meanings followed, for instance that of “a question” or of “a state of embarrassment”, which are very close to the current meaning of a problem. Among the works of Aristotle is that of “Problēmata”, which is a collection of “why” questions/problems and answers on “medical”, “mathematical”, “astronomical”, and other issues, e.g., “Why do the changes of seasons and the winds intensify or pause and decide and cause the diseases?”¹

Problem solving is a complex set of activities, processes, and behaviors for which various models have been used at various times. Specifically, “problem solving is a process by which the learner discovers a combination of previously learned rules that they can apply to achieve a solution to a new situation (that is, the problem)”.² Zoller identifies problem solving, along with critical thinking and decision making, as high-order cognitive skills, assuming these capabilities to be the most important learning outcomes of good teaching.³ Accordingly, problem solving is an integral component in students’ education in science and Eylon and Linn have considered problem solving as one of the major research perspectives in science education.⁴

Bodner made a fundamental distinction between problems and exercises, which should be emphasized from the outset (see also the Foreword to this book).^5–7 For example, many problems in science can be simply solved by the application of well-defined procedures (algorithms), thus turning the problems into routine/algorithmic exercises. On the other hand, a real/novel/authentic problem is likely to require, for its solution, the contribution of a number of mental resources.⁸

According to Sternberg, intelligence can best be understood through the study of nonentrenched (i.e., novel) tasks that require students to use concepts or form strategies that differ from those they are accustomed to.⁹ Further, it was suggested that the limited success of the cognitive-correlates and cognitive-components approaches to measuring intelligence are due in part to the use of tasks that are more entrenched (familiar) than would be optimal for the study of intelligence.

The division of cognitive or thinking skills into Higher-Order (HOCS/HOTS) and Lower-Order (LOCS/LOTS)^3,10 is very relevant. Students are found to perform considerably better on questions requiring LOTS than on those requiring HOTS. Interestingly, performance on questions requiring HOTS often does not correlate with that on questions requiring LOTS.¹⁰ In a school context, a task can be an exercise or a real problem depending on the subject's expertise and on what had been taught. A task may then be an exercise for one student, but a problem for another student.¹¹ I return to the issue of HOT/LOTS in Chapters 17 and 18.

1.2 Types and Kinds of Problems

Johnstone has provided a systematic classification of problem types, which is reproduced in Table 1.1.⁸ Types 1 and 2 are the “normal” problems usually encountered in academic situations. Type 1 is of the algorithmic exercise nature. Type 2 can become algorithmic with experience or teaching. Types 3 and 4 are more complex, with type 4 requiring very different reasoning from that used in types 1 and 2. Types 5–8 have open outcomes and/or goals, and can be very demanding. Type 8 is the nearest to real-life, everyday problems.

Table 1.1 Classification of problems. Reproduced from ref. ⁸ with permission from the Royal Society of Chemistry.

Type	Data	Methods	Outcomes/Goals	Skills bonus
1.	Given	Familiar	Given	Recall of algorithms.
2.	Given	Unfamiliar	Given	Looking for parallels to known methods.
3.	Incomplete	Familiar	Given	Analysis of problem to decide what further data are required.
4.	Incomplete	Unfamiliar	Given	Weighing up possible methods and then deciding on data required.
5.	Given	Familiar	Open	Decision making about appropriate goals. Exploration of knowledge networks.
6.	Given	Unfamiliar	Open	Decisions about goals and choices of appropriate methods.
7.	Incomplete	Familiar	Open	Once goals have been specified by the student, these data are seen to be incomplete.
8.	Incomplete	Unfamiliar	Open	Suggestion of goals and methods to get there; consequent need for additional data. All of the above skills.

Problem solving in chemistry, as in any other domain, is a huge field, so one cannot really be an expert in all aspects of it. Complementary to Johnstone's classification scheme, one can also identify the following forms: quantitative problems that involve mathematical formulas and computations, and qualitative ones; problems with missing or extraordinary data, with a unique solution/answer, or open problems with more than one solution; problems that cannot be solved exactly but need mathematical approximations; problems that need a laboratory experiment or a computer or a data bank; theoretical/thought problems or real-life ones; problems that can be answered through a literature search, or need the collaboration of specific experts, etc.

According to Bodner and Herron, “Problem solving is what chemists do, regardless of whether they work in the area of synthesis, spectroscopy, theory, analysis, or the characterization of compounds”.¹² Hancock et al. comment that: “The objective of much of chemistry teaching is to equip learners with knowledge they then apply to solve problems”,¹³ and Cooper and Stowe ascertain that “historically, problem solving has been a major goal of chemistry education”.¹⁴ The latter authors argue further that problem solving is not a monolithic activity, so the following activities “could all be (and have been) described as problem solving:

• solving numerical problems using a provided equation
• proposing organic syntheses of target compounds
• constructing mechanisms of reactions
• identifying patterns in data and making deductions from them
• modeling chemical phenomena by computation
• identifying an unknown compound from its spectroscopic properties

However, these activities require different patterns of thought, background knowledge, skills, and different types of evidence of student mastery”¹⁴ (p. 6063).

1.3 Novice versus Expert Problem Solvers/Problem Solving Heuristics

Central among problem solving models have been those dealing with the differences in problem solving between experts and novices. Experts (e.g., school and university teachers) are as a rule fluent in solving problems in their own field, but often fail to communicate to their students the required principles, strategies, and techniques for problem solving. It is then no surprise that the differences between experts and novices have been a central theme in problem solving education research. Mathematics came first, in 1945, with the publication of George Polya's classic book “How to solve it: A new aspect of mathematical method”:¹⁵

“The teacher should put himself in the student's place, he should see the student's case, he should try to understand what is going on in the student's mind, and ask a question or indicate a step that could have occurred to the student himself”.

Polya provided advice on teaching problem solving and proposed a four-stage model that included a detailed list of problem solving heuristics. The four stages are: understand the problem, devise a plan, carry out the plan, and look back. In 1979, Bourne, Dominowski, and Loftus modeled a three-stage process, consisting of preparation, production, and evaluation.¹⁶ Then came the physicists. According to Larkin and Reif, novices look for an algorithm, while experts tend to think conceptually and use general strategies. Other basic differences are: (a) the comprehensive and more complete scheme employed by experts, in contrast to the sketchy one used by novices; and (b) the extra qualitative analysis step usually applied by experts, before embarking on detailed and quantitative means of solution.^17,18 Reif (1981, 1983) suggested further that in order for one to be able to solve problems one must have available: (a) a strategy for problem solving; (b) the right knowledge base, and (c) a good organization of the knowledge base.^18,19

Chemistry problem solving followed suit providing its own heuristics. Pilot and co-workers proposed useful procedures that include the steps that characterize expert solvers.^20–22 They developed an ordered system of heuristics, which is applicable to quantitative problem solving in many fields of science and technology. In particular, they devised a “Program of Actions and Methods”, which consists of four phases, as follows: Phase 1, analysis of the problem; Phase 2, transformation of the problem; Phase 3, execution of routine operations; Phase 4, checking the answer and interpretation of the results. Genya proposed the use of “sequences” of problems of gradually increasing complexity, with qualitative problems being used at the beginning.²³

Randles and Overton compared novice students with expert chemists in the approaches they used when solving open-ended problems.²⁴Open-ended problems are defined as problems where not all the required data are given, where there is no one single possible strategy and where there is no single correct answer to the problem. It was found that: undergraduates adopted a greater number of novice-like approaches and produced poorer quality solutions; academics exhibited expert-like approache...

Table of contents

• Cover
• Title
• Copyright
• Contents
• Chapter 1 Introduction−The Many Types and Kinds of Chemistry Problems
• Part I: General Issues in Problem Solving in Chemistry Education
• Part II: Problem Solving in Organic Chemistry and Biochemistry
• Part III: Chemistry Problem Solving in Specific Contexts
• Part IV: New Technologies in Problem Solving in Chemistry
• Part V: New Perspectives for Problem Solving in Chemistry Education
• Subject Index