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A Study of the Development of Programming Ability and Thinking Skills In High School Students

A Study of the Development of Programming Ability and Thinking Skills In High School Students
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  J. EDUCATIONAL COMPUTING RESEARCH, Vol. 2(4), 1986 A STUDY OF THE DEVELOPMENTOF PROGRAMMING ABILITY ANDTHINKING SKILLS INHIGH SCHOOL STUDENTS* I ' D. MlDlAN KURLAND I ROY D. PEACATHERINE CLEMENTRONALD MAWBY Bank Street College of Education ABSTRACTThis article reports on a year-long study of high school students learning com-puter programming. The study examined three issues: I) what is the impact ofprogramming on particular mathematical and reasoning abilities?; 2) what cog-nitive skills or abilities best predict programming ability?; and 3) what do stu-dents actually understand about programming after two years of high schoolstudy? The results showed that even after two years of study, many studentshad only a rudimentary understanding of programming. Consequently, it wasnot surprising to also find that programming experience (as opposed to ex-pertise) does not appear to transfer to other domains which share analogousforrnal properties. The article concludes that we need to more closely studythe pedagogy of programming and liow expertise can be better attained beforewe prematurely go looking for significant and wide reaching transfer effectsfrom programming.Psychologists, computer scientists, and educators have argued that computerprogramming can be a powerful means of enhancing thinking and the develop- '- meut of good problem-solving skills in clddren, in addition to being a powerfulnlelllod for teaclling students fu~idamen~aloncepts in ~nathe~iiatics,hysics,and logistics [I-31. At first glance, the errthusiasm surrounding progranmingseems well-founded. Observations of expert adult progranlniers indicate that * The work reported here was supported by the National Institute of Education (Con-tract No. 400-83-0016). 'The opinions expressed do not necessarily reflect the position orpolicy of the National Institute of Education and no official endorsement should be inferred. O 1986, eywood Publishing Co., Inc.  I 430 1 KURLAND ETAL. prograninicrs explicitly employ important problem-solving strategies such as de-conlposing problenis into niodules, use analogical reasoning, and systeniaticallyplan, code, i~nd ebug their programs. Prograniniing seems to denland complexcognitive skills sucll as procedural and conditional reasoning, planning, adanalogical reasoning [3-71. In addition to problem-solving skills, prograniming utilizes fundamental con-cepts such as variables and recursive structures, which are important in mat11 andphysics. It is well-known that tllese concepts are difficult to teach with tradi-tional media, and their enlployme~itn the functional context of a programniinglanguage may make tllcni mole easily coriiprel~ended 1, 31 . Motivated by this entl~usiasn~or tlle potential of programming, as wellas by the pressure from business and the homes to make students "computerliterate," schools have instituted programniing courses or related activitiesat all grade levels. Yet, surprisingly, there has been very little research to datewllicli has directly addressed the many broad clainis that have been made forprograniming. And in addition, there has been very little researcli exarni~iingwhat students are learning about programming itself as the result of scl~ool-based instruction. We know far too little about what to expect students willlearn in a reasonable period of tinie, how they learn, what conceptual diffi-culties they encounter, wliat forms of cognitive support they may requireto guide them over these difficulties, or wlictl~er individual differences inlearning styles are reflected in progranlniing and need to be guided differentlyin instruction. And beyond wliat rational analyses suggest, we cannot saywith niucl~ ssurance what knowledge and skills that students bring with tlien~wllen they first rneet programming (e.g., natural language competencies; var-ious reasoning skills) are likely to facilitate the acquisition of programmingknowledge and skills.Addressing the issue of how well students actually learn to program inprecollege courses is thus an important matter. It is particularly importantbecause of two relatior~sbetween level of expertise and transfer of learning.First, it is primarily sophisticated programming activities that demand Iiigherlevel tliinking skills and probleni-solving tecl~niques, nd these activities requireexpertise. Tl~ey equire a good model of tlie stucture of tlie language: youcannot write a niodular program and use variables unless you understand howcontrol is passed in the language arld how logical tests o1)crate on outputs ofope~ations. I'l~us, llc tlli~lki~hgkills we 11opc will dcvclol) ;III~ ri~nsfcrout ofprogranlliiing depend upon students attilining certain proficiencics in progrillli-mirig 18). Simple tasks such as a four-1i11cgraphics program to draw a box donot require tlie full range of conlplex reasoning skills that programming is pur-ported to help develop.Second, the transfer of concepts and skills across domains depends on the de- tection of potential similarities between a known and a new domain [9-131.Brown, Bransford, Ferrara and Campione note tliat tliis fact implies differences in PROGRAMMING AND THINKING SKILLS / 431 the abilities of novices and experts to transfer since novices and experts classifysi~liilarities etween tasks differently [14]. Novices will be Inore limited in theirabilities for recognizing proble~n imilarity since they tend to classify tasksaccording to surface clia~acteristicswllereas experts focus on underlying con-ceptual properties or casual structures. For example, Chi et al. examined thecategorization of physics problenis by novice and experts [IS]. Novices cate-gorized problems in ternis of particular objects referred to, terminology given ina problem statement, and physical configurations of problem elements. In con-trast, experts ignored these superficial features of problems and categorizedthem in te~ms f physics principles relevant for problem solutions. Since thenovice and expert represent donlairis differently, they have different inforniationto use in classifying proble~ns, rtd in accessing knowledge potentially useful ina new p~oblem-solving ituation. Similar findings liave been obtained for noviceand expert adult prograninicrs [16].Thus, in programming, even if novices begin to develop an understandingof the workings of the language and to write relatively sophisticated pro-gram, they may represent progran~niing n terms of the surface code, for-niat and syntactic properties of a language, and in terms of particular tasks.Experts, on tlie other Iiantl, are more likely to represent programming prob-lems in terms of the general concepts which underlie particular programmingcollstructs such as recursive routines and variables, the underlying structureof broad classes of problems, the solution strategies wllich crosscut many typesof problems, or routinized plans [I71 or "templates" [18] for solving com-mon progranlnling subproble~ns. Those aspects of programming problem-solving skills we hope will transfer, and that could transfer, involve the generalstructure of tlie problem-solving activity and general concepts. Further, theability to transfer these tecliniques and concepts from programming will de-pend on recognizing problenls in new domains where tlie techniques and con-cepts apply by analogical extension [l 1, 191 . Wl~etlierwe are concerned about students learning to tliink better tlirougliprograniming, or in their learning to program, it is essential to recognizetliat we are in a very early state of knowledge about the psychology of pro- " gramming. For this reason, any work in tliis area, has tlie nature of work inprogress. Tlie teclinologies available to schools, both hardware and software,arc in great flux, ad teacl~ers' i~~tuitionsre being sl~arpened lirougli tl~eircxpcricnccs in Ilelping students Ie;trn program~i~irig,nt1 to think tl~rougl~ro-grani~ning.So, as useful as any new findings ill this area are likely to be fo~tl~e ducator, tllcy must be tre;~ted with cnution. At the sanie time, tlie in-fluence on education of grandiose arid optimistic pronouncements that liavebeen made about the cognitive benefits of programming, and on the ease withwhicli students can learn to program, cry out for empirical assessment, evenin these early days in the field when the terrain changes faster then one's "re-search snapshot" develops.  432 1 KURLAND ET AL. THE PRESENT STUDY To begin to examine more directly some of tlie many claims that are beingmade for and about prograninling we undertook a study designed to investigatethe relation between thinking skills and programming, and to investigate the pro-gramming skills acquired by precollege students. We were interested in the devel-opment of programming skill among well taught precollege students with signifi-cantly more experience programming than most students who have participatedin previous research studies.Our study Ilad three aims. The first was to document the inlpact of program-ming experience on a range of reasoning and math skills. The second was to ob-serve the nature of tlie programming knowledge students attained. The third wasto replicate findings from a previous study [20] that certain of these cognitiveskills predict some aspects of programming skill acquisition.Our choice of concepts and skills to investigate was based on a rationalanalysis of the cognitive conlponents of programming and on correlations, foundin previous research, between particular skills and programming mastery [6]. The tasks chosen involved procedural reasoning, decentering, planning, mat11understanding, and algorithm design.Our particular task designs were based on an analysis of how the target skillsare manifested in programming. Many of the skills we were interested in couldnot be assessed by standard cognitive ability measures, either because nomeasures of these skills exist or because existing measures demand skills in aform inappropriate to progranlnling. For instance, standard tests of conditiorinlreasoning examine comprel~ension f material implication as defined in standardlogic. This is not particularly relevant to tlie use of conditionals in programming;rather the conditional reasoning skill practiced in programming involves reasoningthrough a complex chain of nested condition-action structures. METHOD DesignThree groups of high scl~ool tudents were tested at the beginning and end ofthe school year. One group of students, tlie Ikperimentnl group, was enrollctl intheir second year of progranlming. A second group, the ,%mc.-CP group, lladtaken one year of programming but had elected not to continue. A third group,the No-C'I' group, 11atl IIO experier~ce rogra~ll~llir~g.A battery of postrests admir~istered t the end of the year was intended toassess the cognitive benefits resulting from the programming course, and for the Experirner~tal group, programming knowledge and skill. Performance on thesemeasures was compared among the three groups of students. The pretests ad-ministered at the beginning of the year were selected as potential predictors ofPROGRAMMING AND THINKING SKILLS 1 433 Table 1. Distribution of Subjects in Each Group According to Sex,Grade in School and Grade Point Average Sex Grade G PA Group Male Feniale 10th 11th 12th Mean Range Experimental 11 4 9 3 3 74.3 40-93 No PriorProgramming 9 , 7 8 2 6 78.0 68-96 I Some PriorProgramming 6 7 4 6 3 77.7 46-94 Total 26 18 2 1 11 12 76.6 40-96 I I performance in the programming class. These tests also served as measures of theinitial ability level of students on many of the skills were were posttested. I StudentsAll students for the study were drawn from a large urban public higll scl~oowit11 an etllnically and socio-econonlically mixed student body. The experimentalgroup consisted of a full class of fifteen students who ranged widely in ability asindicated by their grade point average. Control students were selected from apool of volunteers and matched wit11 the experimental students on math back-ground, overall GPA, and grade level. Students in the Some-CP group had takenan introduction to computers through the BASIC language course the previousyear. Table 1 gives the breakdown of the tliree groups by sex, grade, and GPA.' i Programming InstructionStudents in the experimental group liad taken the same introductory courseas tlie Some-CP students. They were currently enrolled in a second, more inten- .. sive prograrnnling course taught by an excellent programming teacher with fiveyears experience.' Class met for forty minutes, five days a week in one of tliescl~ool's computer labs. Over the year students studied six programming lan-guages. Tlley spcnt nine weeks ench onBASIC, COBOL, and Logo and tl~reweeks each on FORTRAN,MACRO,and Pascal.The nun~ber f students reported for results of rerlain Incusures varies since we wcrcunable to administer some tests to one or two stutlenls in each group. 2 The teacher of the Experimental students had a B.A. in Matl~ernaticsrom Yale Univer-sity, an M.A. in Interactive Educational Tecl~nology rom Harvard University, and five yearsof teaching experience. Her students have won the department's prize exam for first yearstudents in each of her five years, and her AP students placed very highly in national compe- ' tition and on the Advance Placement Exam.  434 1 KURLAND ET AL. The nine week Logo section came at the end of the yeu. While the prograln-ming teacher designed and taught the curriculunl for the other five languages, wedesigned, tllcn llatl the teacller teach, the Logo curriculu~n. ur ail11 in designingthe Logo curriculum was to help students develop a richer n~ental nod el of Logotl~a~ltudents in our previous studies seemed to develop. The focus was oncontrol structure. Work was done solely in list processing-no turtle graphicswere taught. In turtle graphics it is too easy for students to continue to generateinteresting screen effccts without understanding the code 1211. 111 ist ploccssi~lgwork, to obtain interesting effects requires a deeper understanding of the lan-guage. This npproacl~ as its own costs-students need to understand more of thelanguage before they car1 (lo 1111rc1l f interest in it.In our design of the Logo curriculunl, we en;pliasized comprehension overproduction. Students were given handouts covering concepts and cornl~iauds,worksheets tllat stressed program cornprel~ension, nd a glossary of Logo primi-tives, written in flow-of-control language (i.e., in terms of inputs and outputs ofLogo con~rilandoperations). And we supplied utilities for file nianagement toencourage a tool-kit approach.We designed a series of weekly projects, each building on the previous ones,so that in principle each project could be a modular tool for the next project.The final project was to program a sin~ple ersion of ELIZA, ll~e3rogr:trtl t11:1tmimics a non-directive psycllotherapist 1221. Topics covered in the course in-cluded Logo commantls (primitives, procedures, inputs, outputs, and outcomes,creating and editing procedures, words, lists, and list processing, input and out-put conlniands, workspace management co~n~~lands,ebugging, trace and errorIiiess:lges, subprocedures, procedures with input variables, naming and creatingvariables with the MAKE coninland, the OUTPUT comrnand, conditionals, andtail ad nlbcdded recursion. Measures The specific rationale and design of each of the tasks used in the study is de-scrihctl fully clsewllcre 1231. A brief review of the tasks is provided below.Pre/es/s - To assess the extent to wl~icll skills acquired in programmingtransfer to other domains, we developed transfer tasks in both "far" and "near"contexts. Our far trnrlsfer tasks (the majority of the tasks), demanded skills webelieved to be deeply ingredient to progran~~ning,ut they bore no obvious stir-face si~uihritics o prog~.;~nlmingi~sks.One near tr;~nsfcr :~sk, n ntltliliotl tobearing deep structural sinlil;~rities o progra~ll~~ling,escr~~hledrog~ii~llnlingtasks in sevc~nlsurface fcilt\lres. 'l'l~e prclcsts wcrc divitlcd illto tl~rce~ypcs:procedural re;lsoning, pI:mning, and nli~llle~~l;~~ics.Proced~trul easot~itrg ests - Rational analysis suggests that programming re-quires instrumental reasoning, particularly procedural reasoning. Designing, conprel~ending, nd debugging programs requires this type of ~oeans-ends easoning.PROGRAMMING AND THINKING SKILLS 1 435 Progranl~ners nust ~nake xplicit the antecedents necessary for differe~lt nds,and must follow tllrough the consequences of different antecedent conditions.Designing and following the flow of control of a program requires understandingdifferent kinds of relations between antecedent and consequent events, and or-ganizing and interrelating the local means-end relations leading to the finalend. Therefore we designed a set of tasks to measure procedural/conditional rea-soning wit11 conditional structures in cornplex contexts. One task was non-verbaland two were verbal. The tasks involved following the flow of control in systemsl~avir~gogical structures analogous to tlie logical structures in computer lan-guages. The systems involved reasonable tliougl~ rbitrary and artificial rules tomake them analogous to a prograniming language and to prohibit students' useof prior world knowledge.Nonverbal Reasoning Task One.This task was designed using non-Englishsyrnbolisnls so that verbal ability and comprellension of the "if-then" connectivewould not be an inllibiting factor for students.Students had to negotiate passage tllrougll tree diagrams having an embeddedcontlitio~l:~ltructure. The task tapped ability to discover wllich go;ds could belegally reaclled given that a set of antecedents were true, and ability to deter-mine the antecedents necessary to reach given goals.Passage tlirough tl~erees ~equiretl atisfaction of conditions set by rlotles inthe tree. Each node required a differing logical combination of various shaped"tokens." Nodes with a disjirtictive structure offered a choice of tokens to beused, and nodes with a cnrtjrrnctive structure required a combination of tokens.Sotne nodes were combinations of disjuncts and conjuncts.T11e task had two parts. In the first part (I'art A), for each question studentswere given a set of tokens and were asked to determine all goals that could beleg;tlly reaclled with that set. The second part (Part B) included additional corn-ponerlts airned at Ilypothetical reasoning and planning abilities. In some instancesnlany routes were legal but students were encouraged to find the most efficientroute. Here we were interested in tlie student's sense for elegant problem solu-tions. In other cases students were required to test a large number of possibil-lies o discover tl~ene legal path.Verbal Reasoning Task One.The first verbal procedural reasoning task wasanalogous to the Non-verbal Procedural Reasoning tasks, but given in verbalform. This task used the "if. . . hen . . . else" structure often found in prograni-ming. The task assessed ability to follow complex verbal instrl~ctions onsistingof t~esletl o~~tliliol~;~ls.tutle~~lslatl to ~~rrtlcrsl:~~~tll~c~icr;lrcllicnl clatio~lse- tween instructions, e.g., tl~tolue condition was o11ly relevant given the out-come ol' I prior co~ltlition.l'llc task involved following ir~structio~lsilllin a 111-ccisely lcfirretl set of tules(deterrnir~i~lg student's tuition from a complex set of rules based on the stu-dent's background and current acade~ilic evel). Like the non-verbal task, stu-dents were given different types of questions to test their mastery of tlie complex  1 436 1 KURLAND ET AL.logical structure. In some questions (Type A) students were given a set of ante-cedents and were asked for the consequence. In other questions (Type R), thegoal or answer was given and the student had to determine what antecedentconditions must have been satisfied. Finally, other questions (Type C) askedwhat are all and only the decisions that must be nlade in order to tlctern~ineparticular outcome given partial knowledge about the conditions. Tl~ese ues-tions required a good understanding of the structure of the instructions. Studentshad to separate irrelevant from relevant conditions and understand the hier-archical relation among conditions.Verbal Reasoning Task Two. This task had a complex conditional struc-ture with a number of goals and conditiorls for satisfaction. Tl~e rohlenl hadtwo conditional structures, in addition to the "if. . . hen . . . else" st~rlcture, tl~at were isomorpl~ic o progr;~rn~ningontlitionals. 'I'l~erewas a "do-until" loopstructure, and a structure isomorpl~ic o an "on gosub" or "Jump match" struc-ture where a matcl~ etween vari;~bles etermines what you do.Planning Task.Several analyses of the cognitive components of prograni-ming isolate planning as a central activity 14-7,241. After defining the problemto be solved, the programmer develops a plan or "structured description" of theprocesses required to solve the problem IS], that will then be written in pro-gramming code. Observations of expert programmers reveal that a major portionof their time is devoted to planning and that they have available many generalplan-design strategies. Pea and Kurland provide an indepth discussion of thenature of planning aa it is manifested in programming [24]. The task used to assess planning skill was s slightly modified version of thatdescribed in Pea, Ilawkins and Kurland 121) (also see 1241). The task involvedscheduling a set of classroom chores: students had to design a plan which spe-cified the sequence in which chores should be completed in order to clean-up aclassroon~ n as little time as possible. The chores were to be executed by a hypo-thetical "robot" who responded to a minimum set of co~nnlands, nd required aspecified amount of time to perform specific actions.This was a computer-based task. A graphics interface depicted a classroom inwhich the chores were to be done. Students gave English co~n~nardso instructthe robot how to clean up the room and the experimenter typed the com~uandsinto the computer. Students designed three plans. After each plan, students weretold how much time the Robot would take to actually execute the plan.The programming and nonprograrnming students were each further dividedinto two subgroups. One subgroup received "feedback" after each plan and theother subgroup did not. Altl~ough ll students were told of the time that wouldbe required to complete their plans, "feedback" students also received a paperprint-out of their plan listing each action and the amount of time it requi~ed.They were also shown a screen display of the classroom, in which a step by stepenactment of the student's plan (the path of the robot as he completed eachchore) was carried out under the student's control. We proposed that there mayPROGRAMMING AND THINKING SKILLS 1 437 be group differences in the extent to which students benefited from the feed-back information.The planning task was administrated for both the pretests and posttest. Twotypes of data yielded by this task were used in the analyses to be reported. Onewas the time required to execute the students' plans. The second was the plan-ning bel~avior f the students. This was assessed by their use of the plan monitor-ing aids, which was recorded by the computer, and the amount of time theyspent thinking about their plan, also recorded automatically by the computer.Math Test. Math ability has been hypothesized to be both a cognitive de-mand and an outcome of programming experience [6, 251. Similarities betweenl~igh chool 1nat11 nd programming exist at several levels. Math and programming1:1ng11ages are both forn~al syste~nswith well-defined syntactic rules. Dot11employ the concepts of v:~ri;hle and algo~ithnl. At the procetlural level, bothmay den~and epresenting relatively rich situations in relatively abstract for-nlalisrns, and then operating or1 these formalis~l~so compute an outcome. Mathword problerlls require extracting essential relations from a prose description ofa situation and representing then1 in ruathernatical terms. Programming involvesgiving an explicit procedural representation of a desired output.Thus we included a math task that we felt would be relevant to programming.Since the math backgrounds of our students varied, and we did not want thetask to dewand special knowledge, we considered the most basic algebraic con-cept-the use of letters to represent variables. All students had enough math sothat this notation was familiar. The task was designed to depend more on theability to systen~atically perate with variables, and on insight during matherna-tical thinking, than on domain-specific matllematical knowledge.These salient similarities guided our task design. We gathered a set of mathproblems that tested either grasp of variables, especially evaluating functions,which is analogous to keeping track of a variable in programming, or ability torelate a syn~bolic xpression to a prose description.We wanted the variables task lo reflect the use of variables in programming.Since values of variables are often passed, modified and printed in programming,we chose problems in wllicll students 11ad to determine the values of variableswhich were defined in terms of other variables. They thus had to evaluate nestedfunctions, following a chain of several variables to reach a constant. To followthe calculation through would be analogous to tracing the value of a variablethrough the execution of a program. Posttests The battery of posttests included mesaures of procedural reasoning, decenter-ing, planning, math ability, and algoritl~rn esign and comprehension. All but thealgorithm test can be seen as measures of "far transfer:" the tests demandedskills and concepts we believed to be ingredient to progranming, but the tests
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