A Study of Problem Solving Strategies Using ATLAS.ti - American

Chapter 9

A Study of Problem Solving Strategies Using ATLAS.ti Downloaded by UNIV OF FLORIDA on December 11, 2017 | http://pubs.acs.org Publication Date (Web): November 20, 2017 | doi: 10.1021/bk-2017-1260.ch009

Tanya Gupta* Department of Chemistry and Biochemistry, South Dakota State University, Brookings, South Dakota 57007, United States *E-mail: [email protected].

The emphasis of this chapter is on the use of ATLAS.ti as a tool for analysis of qualitative data. A brief overview of ATLAS.ti software is provided. The chapter also includes a detailed example of the application ATLAS.ti software for a qualitative research study on the problem solving behavior in stoichiometry.

Introduction ATLAS.ti is a qualitative data analysis package. The history of qualitative data analysis goes back to the 1960s when the mechanization of data analysis began with word sorting in the discipline of humanities, literature and linguistics. However, by 1980s the computers became faster, stronger and easily accessible. The field of social science research evolved with the use of computer aided-research tools that became available in late 1980s. Around 1995, commercially available software tools were developed and were being successfully used in research and dealing with data sets that was unimaginable manually. These early software included HyperResearch, NVivo, Ethnograph, Hypersoft and ATLAS.ti (1–3). In present day and age,these tools find widespread applications for developing research projects conducting a literature review, data compilation, data analysis, and developing reports across various disciplines (4).

© 2017 American Chemical Society Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

Downloaded by UNIV OF FLORIDA on December 11, 2017 | http://pubs.acs.org Publication Date (Web): November 20, 2017 | doi: 10.1021/bk-2017-1260.ch009

This chapter is mainly focused on a qualitative research study on problem solving behavior and highlights the application of ATLAS.ti software in a chemical education research study. ATLAS.ti was first commercially released in 1993, and is in the eighth version for both Windows and Macintosh based computers.There are several approaches to qualitative data analysis and the use of software does not essentially dictate the research methodology involved (5–7). In a qualitative research study, the researcher is the analyst who drives and determines the direction of the study and uses software to help with managing and organizing data, thinking about the data and making sense of data to generate findings and come to a conclusion, or to plan further studies (8). The present study involving the use of ATLAS.ti is on problem solving behavior in reaction stoichiometry. In this chapter, the author is introducing a qualitative data analysis package by providing an example of how such a tool can be used to answer specific research questions by engaging inthr process of data analysis and interpretation.This should be viewed more as an example of using qualitative software to conduct analysis and not as a promotion for the specific software.

A Literature Review of Problem Solving in Chemistry A primary goal of science education is to develop a deep and robust understanding and appreciation of scientific concepts and principles among students. Problem-solving skills are essential for students for them to be successful in their chemistry courses. Student understanding of these scientific concepts and principles can be evaluated through their problem-solving approaches. Problem solving is what you do when you do not know what to do (9). A problem is defined as a gap between where a person is, and where he or she wants to be but does not know how to cross the gap (10). By a careful examination of the student approach to solving chemistry problems, it is possible to know how students learn chemistry, and the conceptual framework within which they operate (11). Students solve similar problems in multiple ways. While some students get hold of the concept and are capable of applying the knowledge to solve particular problems, others find the same concept alien when it is presented to them in a problem format. The conceptual framework of students’ determines which problem-solving strategies are to be used. Students are successful in defining concepts but have a hard time representing their conceptual understanding (12). Some studies have shown the extent to which students are successful in solving various types of chemistry problems. Research on problem solving has particularly focused on: 1. 2. 3. 4. 5.

Successful and unsuccessful problem solving (13, 14) Comparing experts and novices (15, 16) Conceptual versus algorithmic problem solving (17–19) Nature of problems posed – well defined versus ill defined problems (20, 21) Group problem solving versus individual problem solving (22) 134

Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Learning in chemistry has been described in terms of problem solving, conceptual understanding, and acquisition of science process skills (23–27). All chemists engage in problem solving behaviors irrespective of the sub-discipline or field of work. Individuals who excel in their chemistry courses have excellent problem-solving abilities. As made clear in the definition, a problem is not an exercise – exercises have known and relatively easy solutions, are routine in nature or can be referred to as familiar problems (28). Few models of problem solving are well known. These models reported by Polya (11) and Wheatley (9), describe problem solving as a series of steps undertaken by an individual to address a given problem. The steps often depend on the nature of the problem. For example, a well-structured mathematical problem involves understanding the problem statement, developing a strategy for solving the problem, carrying out the strategy, and reviewing/revising the strategy after problem-solving effort. Bodner (28, 29) describes problem solving strategy in chemistry as a sequence of steps that involve reading and re-reading the problem; writing down a tentative strategy for solving the problem; representing the problem using a picture, equation, or a formula to further understand the problem; applying the strategy, trying again if it fails; re-thinking and revising strategy and reviewing progress on solution; writing an answer obtained from the solution strategy; reviewing answer and seeing if it makes any sense with respect to the problem asked; if it does not make sense then starting all over again with a new strategy till the solution is reached. Understanding of problem may happen at any stage during the problemsolving process (30, 31). Problem solving involves seven distinguishable steps that include: 1.

2. 3. 4. 5. 6. 7.

Reading and comprehending the problem statement in ways that one can understand it through rephrasing, simply stating, and using symbols and representations to visualize it. Transforming the parts of problem statements into meaningful chunks Setting goals and sub goals for developing solutions Being selective about information from problem statement Retrieving rules and facts from memory that seem to relate to the problem Achieving goals and sub goals by explicitly or implicitly linking information, facts and formulas and the solution strategy Rechecking path of solution (strategy) and reviewing answer

Several problem-solving stratgeies have been reported (32–34). The steps in problem solving may be similar across diverse strategies employed for problem solving depending on the type or problem, context, and the problem domain. Study of problem solving strategies remains a challenging area and the various models reported by Polya (11) and Bodner (28) help us to look deeper at the these problemsolving strategies employed by individuals or a group of people. In a few studies the problem-solving strategies, the problem-solving success has been tied to the abilities of problem-solvers. Studies on the strategies used by experts (people trained in the content area) and novices (first-timers to the 135 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


discipline) involved using problems that were challenging enough to require more than the mere recall from the experts. Yet these problems offered enough in terms of simplicity for novices to arrive at a the solution (13–15). These studies on experts and novices used different methods to compare problem-solving strategies. One study used HyperCard method to compare balancing equations among high school honors students (experts) and students from a regular chemistry class (35). Another study used a paper and pencil test method to compare differences in student performance on various chemistry problems to classify students as experts and novices (16). Students who demonstrated better strategies and higher problem-solving performance had fewer procedural errors and demonstrated a better conceptual understanding than the novices in these studies. In all these studies on problem solving, experts and novices primarily differ in their strategies based on their prior experiences (15, 16). Experts often classify the problems according to the principles that govern the problem solution and novices tend to classify problems based on their surface features. The experts use knowledge development or forward chaining strategy to solve problems. In the forward chaining strategy, one begins with the information provided in the problem and works his/her way to perform all needed steps to arrive at the final goal (problem solution). Novices deploy means-end analysis strategy, which begins by identifying the problem goal and then finding differences between the goal and the information stated in the problem. The next step in means-end analysis strategy is to seek an equation or a formula that would help eliminate the gap between the problem information and the supposed solution (15, 16). The novice approach is akin to going through several different pieces of a puzzle to find a missing piece and then trying to fit in the very first piece that fits the shape and size of the incomplete puzzle. There is little consideration of whether the piece really belongs to the puzzle being solved. In the expert/novice studies, the experts are able to solve problems successfully worked using the forward-chaining strategy. It is possible that the expert views the problems presented as a routine exercise or has more familiarity with the problem. For novices the path to problem solution is not obvious and immediately recognizable which leads to the slower rate at which novices solve these problems, use incorrect formulas and equations, and take more pauses when asked to present their thoughts in words (during interviews). Unraveling problem solving strategies is however much more complex than the expert-novice paradigm presented by early researchers. Some researchers focused on successful and unsuccessful problem solvers in chemistry and identified several traits of successful problem-solvers (36, 37). According to these studies successful problem solvers tend to read the problem completely and understand the problem objectives clearly before engaging in any problem solving strategy. Once there is a understanding of problem, these problem solvers then write any equation in case of a reaction early on. They try to grasp the problem based on the underlying reasoning and probable solution. Another trait of successful problem solvers is that they do not invoke a formula or an equation until they are certain of being able to solve a problem in chemical terms. They often develop representations and use symbols and 136 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


formulas to represent the chemical species involved in the problem. These problem solvers perform all necessary steps in a specific order to solve the problem; they apply the information provided and infer any implied information (left out from problem statement yet important to consider). While performing all these steps the successful problem solvers frequently check their work to see any inconsistencies, make proper assumptions, and have an adequate understanding of chemical concepts, and principles involved in the problem. Most importantly successful problem solvers can recognize the patterns of problems, use trial and error less frequently, think effortlessly and fluently about their strategy, and are able to express their understanding of the problem and deploy more than a single strategy to arrive at the problem solution (38–40). In general successful problem solvers have advanced knowledge; they use declarative and procedural knowledge; construct appropriate representations; and apply general reasoning abilities that permit them to make logical connections among various problem elements. Successful problem solvers apply more than one verification strategy to ensure that their problem representations are consistent with the facts provided. Their solution is logically bound, their computations are error free, and the problem solved is the problem presented (41). Problem solving strategies among students are developed based on the types of problems that students experience during their education. In chemistry courses students are presented with the end of chapter textbook problems as practice problems. Similar problems are asked during exams and quizzes. These problems might be easy for experts due to their experience and familiarity with textbook problems, yet these may pose a reasonable challenge to students. Such problems invoke quantitative reasoning at the expense of qualitative explanations. Students use rule-based strategy to solve such problems, which rely on memorization of rules, and algorithms that can be practiced, until they can be applied directly to familiar problems (42). Some papers on problem-solving in chemistry have focused on the nature of problems being conceptual and algorithmic or mathematical (12, 17, 19, 26, 43–45). These studies reported that even high achievers struggle when presented with conceptual problems. Successful problem solvers are good at solving both conceptual and algorithmic (formula based) problems and can transfer their skills from one sub-discipline of chemistry to another with ease. Assessment of student knowledge of concept underlying a problem requires asking problems that are effective in eliciting student thinking, representations, and problem solving strategies. Multiple-choice exam problems do not help with assessing such understanding among students unless they are carefully designed. Students with erroneous understanding can select a correct answer choice (46, 47). Usual multiple choice problems rely on rote memorization and short term memory of definitions of concepts rather than in-depth understanding and long-term memory that builds on practice, reflection and critical assessment of ideas. Few studies have also reported that students can learn to solve chemistry problems without understanding the underlying concept. These students often rely on their memory for terms and definitions and apply concepts without any real comprehension (48–50). Such students perhaps go through several episodes during problem solving that involve reading, defining problem, setting-solution, 137 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


and solving the problem. Specifically, algorithmic problems take more time and involve a greater number of transitions from the episodes than the paired conceptual problems (51–54). As evident from a c comprehensive literature review on problem types and strategies, it is evident that despite numerous studies reported on student problem-solving strategies, there is a a need to understand the strategies used by the course instructors to solve chemistry problems, and how do their problem solving strategies compare to graduate students and first year college students. The chapter addresses the gap in this area by using case-study approach to compare the problem-solving strategies used by an instructor, graduate student, and a first year student. The study was conducted using ATLAS.ti software. The next section is covers the theoretical framework of constructivism and Adaptive Control of Thought-Rational (ACT-R) theory that inform this qualitative case-based study.

Theoretical Framework According to the constructivist theory, the process of knowledge acquisition by an individual begins with the input from the environment as detected by senses. An individual actively constructs knowledge from the data obtained by the senses and by the further interaction of this data with the existing knowledge (55, 56). Constructivism has a great relevance for teaching and learning. The process of knowledge construction by an individual is often limited by a zone of proximal development and involves facilitation of knowledge construction by a more knowledgeable peer or an instructor (expert). Specifically the knowledge constructed must fit the reality, thus leading to common knowledge across the group of people. Constructivism focuses on both building and testing of the knowledge that is viable and workable (57–59). Piaget has outlined four stages of intellectual development - sensory-motor, pre-operational, concrete operational, and formal operational. Students reach the formal operational stage by the age of twelve, and complete intellectual development occurs by the age of fifteen. Students at the concrete operational level have struggle thinking about various possibilities and find it difficult to understand the concepts and principles that depart from reality (abstract ideas such as atoms, electrons, and nucleus). The scientific ideas are counterintuitive and cannot be acquired by merely observing phenomena (60–62). The formal operational student has the capacity to think regarding the possibilities and can reason out efficiently what might happen, without any visible aid. The student at concrete level can solve problems that require formal thinking, provided that the student gets an opportunity to deal with the formal concept using some concrete experience that lead to real observations as a special case of the possible (24). According to Herron (24), formal thinkers have some expertise of the subject and display an advanced level of comprehension of chemical concepts as compared to the concrete operational thinkers. While the concrete operational thinkers can 138 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


think only in terms of real and possible, formal thinkers are a step ahead and can think in terms of possibilities or abstractions. The constructivist approach offers an invaluable insight of the learning process according to which, during the process of learning, the learner necessarily reconstructs any knowledge. Constructivist model plays a role of involving the learners in learning a pre-determined body of agreed knowledge (i.e. consensually agreed scientific theories than the personal theories about phenomena). The constructivist model is also helpful in explaining the misconceptions that students bring to chemistry and the resistance of these misconceptions to change toward meaningful learning (62, 63). Highly meaningful learning often relies on problem solving and creativity and is possible in the knowledge domains in which the learner has a considerable well-organized prior knowledge . Adaptive control of Though-Rational (ACT-R) theory: encompasses both declarative and procedural knowledge. It builds on student prior knowledge and states that both the declarative and procedural knowledge are acquired by individuals from their prior knowledge of facts and processes. ACT-R explains three types of learning that includes ability to generalize discriminate and strengthen the knowledge by application and transfer. In terms of generalizability –one’s understanding begins to expand and an individual gains a holistic perspective when he/she come across the same idea or principles several times. Discrimination of understanding arises from the specific application of knowledge in certain areas, and strengthening of knowledge occurs by practice, retrieval and application of knowledge when an individual applies his or her understanding for solving various problems in different contexts. All three – the ability to generalize, discriminate, and apply to strength knowledge leads one to make new connections, and think critically to discard the ones that are not relevant or needed during problem-solving (64–66). ACT-R theory explains the process of knowledge acquisition, organization and application. According to ACT-R theory problem solving takes places within a theoretical space or a mental representation that includes an initial state, intermediate state, and a final state that satisfies the goal. The state could imply some external conditions or internal coding of those external conditions. The process of problem-solving involves an operator, which can be understood as an action that transforms one state into another. The theory assumes that when a problem solver approaches a state for which there are no adequate problem-solving operators, the problem-solver searches for an example of a similar problem solving state and tries to address the problem using an analogy or an example. The initial state of problem-solving is referred to as the interpretative stage. In this stage, an individual recalls specific examples related to the problem at hand and an attempt to interpret these examples. This step invokes declarative memories without any involvement of long-term memory. Reviewing text examples for end of chapter problems would be an example of memory recall stage (67). The interpretative stage may involve considerable verbalization as the problem solver attempts to practice the key attributes of the examples used or from which analogous problem is determined. With procedural encoding of the skills, the verbalization becomes less distinct due to transition from the 139 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


interpretative stage to that of procedural encoding. This stage is referred to as knowledge compilation stage during which the problem solver transitions from the interpretative stage to the procedural stage. The procedural knowledge gets encoded in the memory as production rules that are production-action pairs (68, 69). Anderson provides an example of such production-action pair as “if the two triangles are congruent then try to prove that their corresponding parts are congruent”. The production rules are principally problem-solving operators in an abstract form that can apply across different situations. Using analogy for problem solving can help on in extracting the problem-solving operators and to establish the production rules (70, 71). The amount of practice determines the strength of encoding which leads to a quick access to declarative knowledge and the application of procedural knowledge. The application of a particular production rule is a direct measure of the strength of the rule. Several production rules can be applied for a give problem at a particular time (correctly or incorrectly) and the probability of each production is indicative of its strength. The ACT-R theory thus explains the variability observed in the problem-solving behavior, which is related to the strength of production encoding.

ATLAS.ti: An Introduction to Software ATLAS.ti is a company based in Berlin, Germany. The first version of the software was commercially released in 1993 as a Qualitative Data Analysis (QDA) Package. The software is currently in its 8th version and can be used on Macintosh computers (Desktops), Windows based computers, Android and Apple based devices as a lighter version of the software. ATLAS.ti is versatile QDA package that can be used for any discipline. It facilitates the process of data organization, data description, analysis and interpretation, and developing research reports or literature summaries. The software is ideal for multiple qualitative approaches and handles various file formats such as text, graphics and images, video data, and audio files. The intuitive interface provides proximity to data, participant view and the context of research. It finds several applications such as comparative analysis; collaboration across teams (cloud based) and integrating qualitative findings with quantitative data in case of mixed-methods research studies (5, 72, 73).

A Brief Description of the User Interface The opening screen of software has 13 dropdown buttons on the top panel (Figure 1). It gives information about software version in use, option to create a new ATLAS.ti project or to import an existing ATLAS.ti project from the computer (the left bottom of the screen). The right panel of screen includes any projects that exist or have been created. If there are no existing projects then this panel appears as a white screen (73). The drop down buttons include: 140 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


1.

ATLAS.ti (About ATLAS.ti, License information, updates, services, option to Hide ATLAS.ti and Quit the program when done using it). 2. Project: This is where a new project is created or uploaded. It includes options such as new, open, open recent, close, save, rename, renumber documents and quotations, info, import project, import iPad project import survey, export project, export project to XML, and print 3. Edit: is used to edit project and includes buttons to undo, redo, cut, copy, paste, paste and match style, delete, select all, deselect all, rename, format, find, spelling and grammar, substitutions, transformations, speech, start dictations, emojis and symbols. 4. Document: this is for all the documents that are for a project on ATLAS.ti. It includes options to import documents, show document manager, show document group manager, and output button to generate a list of documents and associated groups or list of document groups and their members 5. Quotation: A quotation is related to segments of text or phrases in the documents that are coded. It includes buttons such as New from selection, Add Coding, Quick Coding, Code in Vivo, Show link manager (links code and quotation), Show Quotation Manager, Output button that generates output or summary of the Commented quotations by documents, Quotations by Code, Quotations by Code with comments, Quotations by Code (Alternative view). 6. Code: This dropdown button includes several options for qualitative coding and output of codes for example new code(s), new smart code, auto coding, show link manager, show relation manager, show code manager, show code group manager, output for Codebook, List of Codes and Associated Groups, List of Codes by Documents, List of Codes Groups and their Members, Tag Cloud, Tag Cloud with Code Colors 7. Memo: is a brief description of document or data or a methodological note. The dropdown menu here includes options for creating a New Memo, Show Memo Manager, Show Memo Group Manager, and Output for All Memos Including Content, List of Memos and Associated Groups, Memos with Content and Linked Quotations 8. Network: This drop-down button displays the links. Users have the choice of creating a New Network, Show Network Manager, and to generate Output that includes List of Code-Code Links with Comments, List of Hyperlinks with Comments, List(s) of Memo Links 9. Analysis: The analysis dropdown menu is for performing a few quick analyses by using the Word Cruncher to generate a summary of the frequency of words, Code Concurrence Table, and Code Document Table. 10. Tools: the tools button provides qualitative researchers the ability to gain a quick overview of project through the Project Explorer button and to manage users by selecting options to Change User or displaying users via Show User Manager. 11. View: The View button contains various options that one would expect in a tools button. Here a researcher can Hide Toolbar, Hide 141 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Drop-Down Lists on ATLAS.ti, Hide the Navigator, Hide Margin area, Hide (Document) Inspector, Sort (a document) by Number and Name and Enter Full Screen Mode. 12. Window: The Window button performs all functions related to a window or a tab for example: Minimize, Zoom, Duplicate Tab, Move Tab to a New Window, New Project Window, Bring all to Front etc. 13. Help: this button is for seeking help on ATLAS.ti with ATLAS.ti Help button and for sending feedback on the ATLAS.ti. On clicking it opens the email function and gives user an option to drag the specific document for which the user has technical issues. It generates a brief report as an email text that contains information about user computer (such as CPU model, old ATLAS.ti crash logs, free disk space etc.) as a file named General.atlinfo. Another file "Projects.atlinfo" contains information on the project database, including code names etc., but excluding memo, comment, or document content. Such user data is used for technical improvement of the software.

Figure 1. Overview of the user interface for ATLAS.ti.

Research Method: Data Collection and Analysis The study on problem-solving strategies involved comparative qualitative case study approach with a purposeful, stratified sampling of three case units out of 51 interviews that were conducted in a mid-western university (73). Comparative case studies were used to study the similarities, differences and patterns across two or more cases that share a common focus or goal in a way 142 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


that produces knowledge that is easier to generalize about fundamental questions (74). In this study the goal was to determine and compare the problem-solving strategies used by college-instructor, graduate student and an undergraduate student. The participants’ identities were coded for anonymity during the entire research process. The instructor in this study was coded as Tim, the graduate student Matt, and the undergraduate student as Zach. Overall nine-first year graduate teaching assistants, four college-professors, and 38 undergraduate students participated in this study. The samples case-units were identified based on the proximity of their solutions during in-depth think aloud semi-structured qualitative interviews to compare the strategies used by each representative particpant. These interviews involved general questions on the prior learning experiences of participants, and their views of science. During interview each participant was asked specific questions on stoichiometry based problems and prompted to gather information regarding the strategies being used by the participants (sample problem is shown below). Each participant was provided with a worksheet while engaging in problem solving during the interview. Each interview was about 45-60 minutes long. The interviews were transcribed verbatim. Stoichiometry problem asked: Octane (C8H18) is a component of gasoline. Complete combustion of octane yields H2O and CO2? Incomplete combustion produced H2O and CO, which not only reduces the efficiency of the engine using the fuel but is also toxic. In a certain test run, 1.000 gallon (gal) of octane is burned in an engine. The total mass of CO, CO2 and H2O produced is 11.53 kg. Calculate the efficiency of the process; that is, calculate the fraction of octane converted to CO2. The density of octane is 2.650 kg/ gal (75). In order to solve this problem one needs to: a) Consider the products of complete and incomplete combustion b) Write equations for two processes c) Compare mass of products from complete combustion to that of incomplete combustion to determine the fraction of octane converted to CO2 or the efficiency of process Data preparation for using software: A key requirement of ATLAS.ti software is data preparation. Data input to the software must be in the form a soft copy, placed in a single folder in a specific location on the computer, and saved as a unique filename. In this study the folder was named problem-solving strategies. After, saving all data files in a single folder in documents, the documents were uploaded in the ATLAS.ti software into a single hermeneutic unit (HU) as primary documents. (p-docs) A Hermeneutic Unit refers to the analysis project and it includes all the documents in various file formats that are a part of the research project. Both text and media files can be uploaded and qualitatively coded in ATLAS.ti. Text documents (.doc files) and scanned PDF worksheets were uploaded as primary documents. The primary documents are the various sources of data and information for a given project, in several formats. When the documents are uploaded the default software file name for the project ends in .hpr8 which has all the documents and the 143 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


work that is conducted on ATLAS.ti. The filename indicates the single separate project with a specific focus or research questions. For simplicity and ease of use the HU was saved in the same folder in which all the primary documents are located. ATLAS.ti provides possibility of organizing the documents into groups using the document group manager dropdown function under the documents button on the main menu. In this study, the files were grouped under the categories of the instructor, student, GTA. Qualitative Analysis: ATLAS.ti serves as a tool to engage in qualitative research to: a) highlight the accounts of the study participants or of phenomena based on the data collected b) analyze the content of the accounts by comparing, connecting and examining the context, and c) to produce an in-depth description of these accounts succinctly in terms of the findings based on the comprehensive examination of the data. Qualitative research is researcher driven. This means that the researcher plans the complete study, its purpose, and determines the methods of the study. The software is a tool for data organization and analysis but it does not perform analysis magically by any means (5). For each document a memo was prepared that contained a brief description of the document. For example, the memo of undergraduate student contained information about the date of interview, participant background, and duration of the interview.The process of coding the problem-solving study involved assigning codes or conceptual categories to the data. The first round of coding involved reading each file carefully to assign categories (also called labels or codes) to segments of the data also called as quotations. Qualitative coding is an iterative process, which means there are several cycles of coding that involve careful review, revision and merger of codes. Figure 2 displays qualitative coding in ATLAS.ti. The codes are on the right side of the window with vertical bars displaying the quotations from the data that were coded. These codes correspond to the interview data that contains the specific stoichiometry problems. Four kinds of coding actions can be performed in ATLAS.ti. These include the basic function of adding code, smart coding, auto coding, and quick coding. Auto coding can also be used searching for a phrase or keyword in all documents or a specific document; selecting codes from the pre-generated code-list in ATLAS.ti and the code to the quotations that return the matched phrases. Recently used codes can also be added to the chunks of data by clicking on “add last used codes” while coding. In-vivo coding is helpful when a specific statement or segment of data stands out and can form a conceptual category by itself. In this study open coding was done using the add-code function. Several codes were assigned during the first cycle of open coding (Figure 2). The initial coding process was open and descriptive. As the analysis progressed the codes were refined and merged to generate the specific strategy for each participant. In order to achieve this goal the code-group manager function of ATLAS.ti was used to categorize similar codes from various coded documents for developing a code-hierarchy. The final coding was more patten based and thematic in nature. Total 30 codes were left 144 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


after deleting duplicate codes, renaming and merging codes. The data was shared with an independent coder with the list of initial codes separately. The inter-coder agreement was found to be about 87% in the first coding cycle (25 agreements/ 30 codes). Overall analysis settled down to 24 codes that led to three major problem-solving strategies that were identified (discussed later).

Figure 2. Initial coding.

During coding analytic memos were developed for various codes and quotations using the memo function of the software. These memos are a researcher description of codes, quotations, and thought processes about the analysis. For example, a memo in this study involved journaling of the patterns that were noticed for a problem-solving strategy and briefly described the quotations or data components that seem to contribute to the observed pattern. In the final stage of analysis network views were generated for codes and memos using Network button (presented in problem-solving strategies). The network views involve links that are created by connecting codes and data segments. Strong links among codes helped determine the specific problem-solving strategy used by each participant by careful examination of the problem solving pathways. The codes were viewed in network view as nodes. The nodes in network link are connected based on the strength of their relation and correspondence with other codes, and related quotations in the data. These interactions indicated whether a code is a part of another code, is associated with a code, is a cause of, or contradicts a code. 145 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Findings: Problem-Solving Strategies Generated by Network Views In ATLAS.ti the quotations, codes, and memos can be connected together as link objects. Nodes and links form the two basic components of network views. Nodes include all objects in a HU such as codes, quotations, memos, primary documents, families and also network views. Different icons can be used to visualize codes (audio versus video based quotations). A network view of codes represents the relations between different codes in a project in the code family. The neighbor in network view includes all objects that are directly linked to a node. One can use Command+N keys to view all neighbors of the nodes for a code. Network views also include co-occurring nodes. These co-occurring nodes include all objects that are assigned to the same or neighboring quotations in the project. The network views can be created from either the existing network ties in the project or generated as a completely new network view from the beginning by clicking on Networks drop-down menu and then clicking on New Network View. One can import the nodes one wants to add to this new network. By clicking on nodes, one can select import nodes function and select as many nodes one needs. The layout of these nodes can be adjusted by selecting the semantic layout and organize the network view. The objects can also be directly dragged and dropped in the network view window. One of the important aspects of creating network views is that whenever existing network ties are used or the new networks are created, making changes by renaming, linking or deleting the network nodes impacts an entire project. For example, while creating a network view of the researcher changed a code name, the change will occur for that particular code in the entire project. The network views can depict both weak and strong links (for example if a step is a part of, cause of sub-part of a concept or a problem-solving strategy). Strong links exist between codes and quotations and are created when a researcher links one code with another or hyperlinks data segments through codes. Strong links have independent properties and are definable. Weak links are adjacent segments or neighbors to codes in the data. These are not independent and are indirectly connected to strong links. The analysis of problem-solving strategies of participants led to identification of three unique strategies as demonstrated by the network views. Tim’s (instructor) approach to problem solving involved reading the problem statement and seeking problem goal. He made frequent use of labels to determine the process in the given problem. Tim proceeded to use symbolic representations for the problem once the nature of the process was known. He constructs a mathematical model based on the information provided in the problem and identifies the gap based on this information, and any other crucial piece of information that may be helpful for crossing the problem-gap.

146 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

So the way I would model this mathematically is….mass of H2O from …reaction A plus the mass of H2O from reaction B plus mass of CO from reaction B plus the mass of CO2 from reaction A and those all together, it will give us 11.53 kilogram (P1:8) Tim explicitly identified his overall problem-solving strategy as a guess and test strategy.


So that’s one piece of the puzzle that those products have the sum to 11.53 kilograms…. Well I will assume that the octane may react, lets say all of the octane reacts. We will just make that assumption. I will do a guess and test strategy here that’s sort of zero in on it (P1-9). Tim splits the equation for the reaction to determine the amount of products generated for both complete and incomplete combustion of octane in order to arrive at the solution. He performs lengthy calculations for the amount of products that would be form for the two processes and hits a dead-end (see Figure 3). Tim then improvises his strategy from here. He looks at the data he generated by performing calculations and engages in logical reasoning to determine the solution. Tim continuously evaluated his solution by asking logical questions that helped him connect various numbers he had generated while attempting to solve the problem. For example, when he finally gets stuck then he questions that “if 100% octane reacts to give 11.93 kilograms of products then how much of the octane reacts to give 11.53 kilograms?” The quote from Tim as below shows his frequent questioning to evaluate his solution. If there is 2.650 Kg how much a 2 kg react completely with equation A and then 0.650 kg react incompletely with equation B. So label this A and B …and then if I want to, I can determine the individual masses of water, and CO2 and CO and see if that sums up to a 11.53 kg as total. That wouldn’t be an ineloquent solution but it is a solution that works where you could sort of work your way down to see if, what is the optimum amount. (P1-11) Matt (graduate student) starts with reading objective of problem and then searches for key information provided in the problem. Matt identifies the problem to be related to Stoichiometry. There is some uncertainty about the problem domain as evident from the statement made by Matt. “The three things [in problem statement] are probably going to be a key to finding out is how much octane goes in and how much carbon-dioxide is made. I see it as a bit of stoichiometry” (P2-4). Matt takes a logical approach problem-solving (Figure 4). He views this problem as being domain specific too, and argues that because it is a stoichiometrybased problem, he must be able to calculate the theoretical yield. He then develops a road map for problem solving that emphasizes the expected yield. Matt makes 147 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

a logical assumption, that in a perfect problem he should be able to determine the difference between the amount of products generated and the expected (theoretical yield) to calculate the actual mass of products for both complete and incomplete combustion of octane.


I will calculate the expected yield is and assume that this is a perfect problem. I will take the difference from that and subtract the difference and the difference in the mass of what came out can be used to calculate the CO and CO should be lighter than CO2 so it should get a numbers bigger than 11.53 (P2-9)

Figure 3. Instructor’s approach to problem-solving. Matt reasons that since carbon dioxide is heavier in mass as compared to CO, the amount of carbon dioxide produced will relate to the efficiency. Matt then proceeds to set the reaction equation for complete combustion and performs calculations for the moles of products (water and carbon-dioxide). He then goes back to the problem statement and draws several comparisons for the masses obtained using the law of conservation. According to Matt’s logical approach, the actual yield is a fraction of the theoretical yield of the products and that should help him determine the final solution. So I am just checking to see if these numbers I am pulling out make logical sense because my reaction from the start is only being containing this much weight so that in the end it should contain the same weight and its gaining a lot of weight and I am not sure if this weight really exists on the 148 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


left hand side. This might work out after all. I don’t have too much time to think about this, means I don’t have to try crazy things, I can actually logically reason it out. (P2-21)

Figure 4. Graduate student’s approach to problem-solving. Zach (undergraduate student) mainly uses rule-based strategy for problem solving. He starts with the reaction equation and writes two separate equations for both the complete and incomplete combustion. Because it says calculate the efficiency of the process. So it is out of zero to 100, but I know that if it converts all of it to CO2 that is 100%, if it does not do a complete combustion and only makes CO, is that 0% efficient, or what is that? (P3-5) Zach focused on the surface features of the problem. He wondered what he needs to do with the numbers provided in the problem to find the efficiency of the reaction. His approach involved some deductive reasoning. For example, Zach thinks that he will get two numbers when solving for the amount of carbon dioxide – a bigger number and a smaller number (incomplete combustion) for maximum and minimum efficiency of the process. It would be the same as using the number of moles I’ll just substitute my number of moles of C8H18 that I found, and figure out how many moles of everything else that I would need…To get it to grams I have to multiply not divide. I can convert number of moles of each of those into grams and see how close it is to the original value and see about what an answer would be (P3-9). 149 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Zach’s argument in solving this problem was that no matter what happens the amount of water produced does not change. During the entire problem-solving process Zach fixated on the amount of carbon dioxide and carbon monoxide and his reasoning stemmed from the reaction equations he wrote. Each time Zach lost his path, he came back thinking about the rules he had learned in his chemistry courses to solve stoichiometry problems (Figure 5). Just looking at my numbers, trying to remember any equations. I’m thinking I can take the mass of the reactants that were actually produced over the amount it would have produced under fully complete combustion and that would give me an efficiency percentage. And that will tell me what percentage of CO2 to CO Yeah. So, 11.53 kg over 11.94 kg is 96% efficiency

Figure 5. Undergraduate student’s approach to problem solving.

Discussion and Conclusions The study provides an insight into the problem-solving strategy used by three participants who are at completely different levels of their experience and exposure to the domain principles and problems in chemistry. A summary of the problemsolving strategy of three case units in this study is provided in Table 1. 150 Gupta; Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and Avenues ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Table 1. Comparison of Problem-Solving Strategy of Instructor, Graduate Student, and Undergraduate Student Instructor

GTA

Undergraduate Student

Schema

Well developed and strongly connected and helps in meaningful interpretation

Developed but not connected

Loosely connected (formula of ethanol)

Domain Principals

Frequently used during problem solving

Somewhat–needed probing

Minimally used

Strategies

Flexible and adaptive Analogical guess, test and revise

Logical Reasoning Strategy for connecting gaps

Rule based and partly deductive strategy – from what is to what should be.

In this study it was found that the instructor, Tim had a well-developed and strongly connected schema of underlying stoichiometry concepts and principles. This helped Tim make meaningful interpretation of the problem statement. He frequently referred to the domain principles during the problem-solving process. Tim was flexible and adapted his strategy on the go. His guess and test, and revise strategy involved frequent use of analogies such as puzzles, bridge and models for problem-solving. In case of Matt, his schema of concepts and knowledge of the domain seemed developed but not as strongly connected as Tim’s, though he had a better understanding as compared to Zach (undergraduate student). His domain needed some probing during the interview process (tell me what are you thinking here, you seem stuck on this can you explain what you are applying?). One would expect graduate student to have a far better understanding of domain principles, but Matt did not display that. Matt used the logical thinking strategy for connecting the gaps in his problem-solving process. He frequently used the word “logic” and “logical” while thinking aloud. Zach uses a rule-based strategy to solving stoichiometry problems. It is common for novices to use rule-based strategies. Zach frequently mentioned rules and principles he had used in solving stoichiometry problems. He relied on his memory to recall facts, formulas, and rules during entire problem-solving process. Zach’s mental schema of stoichiometry seemed to be rather weakly connected. He uses the incorrect formula for ethanol in one of the stoichiometry problems, yet his rules and approaches to the solution were correctly applied. Perhaps, due to the weak schema of various components needed to solve the stoichiometry problems, Zach often doubted his solutions and his final answer. He minimally used domain principles, and when used these were not correctly applied. The rule-based approach was used to set-up equations and to calculate the moles of products from the balanced chemical equations in-case of Zach.



The three strategies identified in this study include guess, test, and revise strategy, logical reasoning strategy, and rule-based strategy for a specific stoichiometry based problem. All three particpants had similar solutions, yet they took a different approach to solving problem. This can be attributed to the development of sound schemas and understanding of domain principles in case of instructor as compared to that of and undergraduate student, who do not display their ability to generalize and discriminate the problem and their prior knowledge. The understanding of undergraduate student seems very limited to rules and he lacks the holistic perspective of having experienced similar problems multiple times as compared to the instructor.

Limitations, Implications, and Further Research The study highlights the problem solving strategies used by diverse participants who have a completely different level of experience and exposure to chemistry, yet all three were successful in solving the chemistry problem presented in this study. The chapter includes several key functions of the ATLAS.ti software that were employed for this study from data organization to data analysis and interpretation. The study is limited to three case units who were successful in solving a specific set of stoichiometry problems. It is also limited in terms of the use of the various features of the ATLAS.ti program. Despite its limitations, the study demonstrates the importance of the role of software in qualitative studies, and that the problem solving in chemistry and perhaps other areas rely on conceptual knowledge backed by the use of rules, analogies and common sense. The software helped with identifying the trail of problem-solving strategies and keeping track of the entire qualitative research process (codes, quotations, and memos). The software also facilitated the triangulation of interview transcripts and the worksheet data and generating inter-rater reliability by sharing files through group project feature of ATLAS.ti. Throughout the analysis process there was a closeness of the researcher to the data and the participant view of the problem solving strategy. The context of the study was always present because of easy access to research questions, the first impression of the researcher, and the data side-by-side within the software. Though the study focused on three participants who were successful in solving a stoichiometry based problem, it leads into further research on the successful problem-solving strategies employed by a diverse group in the sub-disciplines of organic and inorganic chemistry. The author is studying problem-solving behavior in this area in organic and inorganic chemistry by using computer simulations developed by her research group (76).


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A Study of Problem Solving Strategies Using ATLAS.ti - American

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