# [IEEE 2013 Learning and Teaching in Computing and Enginering (LaTiCE) - Macau (2013.3.21-2013.3.24)] 2013 Learning and Teaching in Computing and Engineering - Teaching Algorithmic Thinking Using Haptic Models for Visually Impaired Students

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Teaching algorithmic thinking using haptic modelsfor visually impaired studentsDino CapovillaDepartment for InformaticsTechnische Universitat MunchenMunich, GermanyEmail: dino.capovilla@tum.deJohannes KrugelDepartment for InformaticsTechnische Universitat MunchenMunich, GermanyEmail: krugel@in.tum.dePeter HubwieserTUM School of EducationTechnische Universitat MunchenMunich, GermanyEmail: peter.hubwieser@tum.deAbstractThe Convention on the Rights of Persons withDisabilities demands inclusive education at all levels, includingfree access to education for disabled people and leading to anincreasing heterogeneity of classes for teachers. Most of the toolsused to teach algorithmic thinking and basic programming areoriented visually and hence badly or not at all usable for visuallyimpaired.In this paper we propose a new method to introduce algo-rithmic thinking using a haptic model (e. g. LEGOTM plates andbricks) suitable for all students. We evaluated the method in acase study with 5 blind students, teaching them three basic searchalgorithms: linear search, binary search, and lookup in a binarysearch tree.It turned out that the haptic method facilitates the under-standing of the underlying algorithmic ideas. Moreover it hasthe advantage to inhibit the common problem of thinking ofmany steps concurrently, because it forces the students to carryout the steps with their hands consecutively. This also facilitatesthe transfer from the model to the source code.Our new haptic method is a suitable way to teach visuallyimpaired students basic algorithmic thinking. But furthermoreit is also a promising approach for sighted students, because itaddresses yet another sense.I. INTRODUCTIONComputer science plays a prominent role for visually im-paired students (VI)1. As a regular subject, computer scienceis nowadays also taught to VI in mainstream classes in mostcountries with a functioning school system. This is one of theconsequences of the adoption of the Convention on the Rightsof Persons with Disabilities: The UN General Assembly statedin Article 24 that all states parties shall ensure an inclusiveeducation system at all levels [1]. Furthermore, computerscience is a popular major choice for high school studentswith disabilities planning on going to college [2].Moreover, computer science is essential for the develop-ment and availability of most assistive technologies [2]. Theyare crucial for educational inclusion and also for an equalparticipation in society [3]. Assistive technologies allow VI toread and write in a normal way and to access information. Butassistive technologies and the knowledge how to use them arecertainly not enough for a successful inclusion.1VI is used in this paper to refer to visually impaired and blindpeople / students in order to increase readability.VI students in inclusive education systems daily feel thatthey are different, because they are usually alone among non-disabled people. Their physical difference leads to a strongpressure to adapt. They do not like to be different, and theydo not like attention drawn to them [4]. A study by Rodney [5]shows that they are often placed in atypical educational andsocial situations and therefore they view assistive technologiesas stigmatizing obstacle for the inclusion. Such atypical situ-ations arise e. g. from their technical aids, special educationteachers, or fundamentally different teaching concepts.In computer science, algorithmic thinking is a basic abilitythat is fundamental for successful programming. Some stu-dents do not know how to program, because they do not knowhow to create algorithms, mainly due to their lack of generalproblem solving abilities [6]. Gomes et al. [6] identified threemain problems: Relating knowledge, problem understandingand reflection about the problem and the solution. Becauseof the uncommon perception and therefore the world viewof VI, especially the task of relating knowledge can differsubstantially in comparison to sighted students. This problembecomes even more apparent in inclusive classes.Sighted teachers and students tend to rely heavily on dia-grams to facilitate the comprehension [4] and the majority ofproblem descriptions are based on visual media like sketches,graphics or animations. However, VI students need descrip-tions that address more than only the visual sensory channel.Providing these would be an advantage for all students,because the more senses that are activated, the more likelyit is that information will be encoded [7]. Reflecting about thesolution after they understood the problem should then be thesame challenge for VI and sighted students.With this paper we propose an inclusive teaching methodto introduce algorithmic thinking. In contrast to many visuallyoriented methods that cause atypical situations for VI, we useda haptic model (toy building bricks and plates) suitable forboth, VI and sighted students. Of course we do not intend tosubstitute the several approved teaching methods to introducealgorithmic thinking. We intend to add a new method thatallows teaching this topic in mainstream classes that includeVI students.There is only a very limited number of people who havea visual impairment. Therefore the testing groups in this area2013 Learning and Teaching in Computing and Engineering978-0-7695-4960-6/13 $26.00 2013 IEEEDOI 10.1109/LaTiCE.2013.141672013 Learning and Teaching in Computing and Engineering978-0-7695-4960-6/13 $26.00 2013 IEEEDOI 10.1109/LaTiCE.2013.14167naturally are not very big. We proved in a field study with fiveparticipants that this teaching model is a suitable approach.II. BACKGROUND AND RELATED WORKEducational software based on Robot Karel [8] and relatedvirtual robots is often used to introduce algorithmic thinking.Califf et al. [4] describe several problems they encounteredwhen using this kind of software to teach one VI student. Theirfindings are in compliance with our experience. VI studentshave the problem that they cannot or only hardly see wherethings are on the screen. But to follow the logic, it is necessaryto see the whole visual representation on the screen and notonly tiny portions. Therefore, the use of Robot Karel or similarmethods is not an adequate teaching method for VI.Another trend in teaching algorithmic thinking is to use realrobots which can be programmed by the students [9]. Theseconcepts are based on the idea that the attention of studentsis attracted by real world objects which can be touched andmanipulated using the computer. For example Barnes [10]investigated the use of LEGO MindstormsTM controlled withJavaTM in introductory programming courses. Ludi [9] andher team developed a Java programming tool to make LEGOMindstorms accessible. This approach can be helpful for VIstudents who are able to see the actions of the robot. But ifa VI student is able to see the behavior of the robot, he willprobably also be able to use the regular control tool (at leastusing a text-magnification tool). Entirely blind students areexcluded from the use of such robots.Various researchers developed target-group oriented soft-ware for VI to learn programming, ranging from adaptationswith reduced instruction set [11] to new programming lan-guages [12]. Kopecek [13] created a source code generatorfor user dialogs (initially developed for VI) and proposedit for the use in mainstream classes. The system asks theuser step by step the required information and creates thecorresponding source code autonomously. The developmentof additional learning software is interesting, especially ifsighted students can benefit from those products as well.Unfortunately, the former methods are not very well suited forinclusive education. In mainstream classes it does not makesense to substitute a standard programming environment onlyfor VI. This would require two different teaching concepts,contradicting inclusion and leading to atypical situations forVI.Another method to introduce algorithmic thinking and com-puter programming, has been described by Bigham et al. [14]and also by VanDeGrift et al. [15]. VI students were pairedwith computer scientists who helped them to implement theirideas and to debug the generated code in a real programminglanguage. Individual coaching is unfortunately inappropriatefor inclusive teaching, because the students learn in individu-alized learning situations with little social contact to peers.The use of haptic models is common in teaching VI,particularly in special education schools. Such models areoften the only possibility to make structured informationaccessible. Typical examples are geographical maps, buildingplans, function graphs and of course Braille2. These modelshave also their weaknesses. When Califf et al. [4] tried to teachbinary trees using Braille examples, they realized that evenover-sized Braille paper could accommodate only small trees.Francioni and Smith [2] described the problem that a hapticmodel allows only a static representation (snapshots). Withsnapshots it is not possible to describe the dynamic behaviorof a system. Therefore, they propose to encode one diagram asa series of tactile versions, to make the contained informationaccessible in several layers.Another problem is the immutability of the models. Allstudents are restricted to the ideas and experience of theteacher who prepared the materials. But given solutions willnot support to develop the individual problem solving ability.III. METHODOLOGYOur proposal is based on the following key issues that derivefrom the problems described above and about ten years ofinclusive teaching experiences of the authors: VI are supposed to have learned the individual handicap-specific techniques (e. g. screen reader, Braille display,display modes) outside the ordinary class in specialtrainings. This includes safe handling of handicap-specificinput and output devices (especially the keyboard), of thegraphical user interface, and the basics of file manage-ment. This compensates most of the differences betweenthe handicaps3 and removes the disadvantage comparedto normally-sighted students, who are able to tap the basicuser skills more intuitively. Lessons should address more than only the visual sense.This means that teaching should address the haptic orauditory sensory channel, too. We believe that olfactionand the gustatory sense are hardly usable. Teaching concepts should basically be beneficial for allstudents. Teaching concepts that are useful only for VIshould be excluded. VI students should get just the necessary and not moreattention compared to other students.Our proposal uses toy building bricks and plates. They aresupposed to help all students understanding the given problemby recreating the initial situation and the steps of the solutionon the plate. We assume that these tools are known by most ofthe students and are able to attract their attention. The hapticmodel is suitable for both, VI and sighted students.To evaluate the method, we chose to use it for teachingprogramming novices three basic search algorithms (linearsearch, binary search and lookup in a binary search tree).These algorithms are very general and appear in many applica-tions. Furthermore, they show several fundamental principlesof computer science (e. g. lists, loops, recursion etc.). But still2Braille is a haptic system of reading and writing for the blind and visuallyimpaired. Each character (letter, numeral or punctuation mark) is representedby groups of 6 dots. Depending on the character a set of dots are raised andcan be interpreted by touching with the fingers.3Visual impairments differ widely. They range from total blindness, throughvisual field loss, to light-dependent handicaps.168168Fig. 1. Setup of the haptic model to teach Linear search in sorted list andBinary search in sorted listthe algorithms seem simple enough to be understandable byprogramming novices after a few hours of teaching.For our proposed teaching method we will now describethe necessary teaching material, the contents of the lesson,and realization of the experiment.A. Teaching materialThe method requires only little material, which can bebought inexpensively in every toy store or brought fromhome, if already available. All that is needed are some toybuilding bricks and plates (e. g. LEGO) to be used as hapticrepresentations of lists and a binary tree. In order to provide ahaptic list of numbers we stuck red tactile numbers on whitebricks. The high contrast ensured that all students would beable to recognize the numbers.Additionally, we developed 3 spreadsheet for the algorithms:1) Linear search in sorted list contains 1000 sorted num-bers in A1:A1000.2) Binary search in sorted list contains 1000 sorted num-bers in A1:A1000.3) Lookup in a binary search tree contains a representationof a binary search tree, using combined cells (i. e. aparent node is twice as wide as its child nodes). Thetree has 6 levels, 63 nodes in total, and 32 leaf nodes.All values were drawn randomly from {0, . . . , 9999}.B. Lesson contentTo teach Linear search in sorted list, each VI student gota plate which we prepared to haptically represent a sorted listof 6 numbers. This is illustrated in Fig. 1. We explained theidea of the linear search in sorted lists. Then the student hadto check if a number is represented in the list. Afterwards theyhad to repeat the same algorithm in Spreadsheet 1.To understand the idea of Binary search in sorted list thestudents again had to interpret each stud in the first columnas a number in a sorted list. This time they additionally hadto put one 4x1 brick in the first row and another such brickin the last row, next to the first column to serve as markers.We asked them to think about a better solution to check theexistence of a number in the sorted list using the two bricksas upper and lower limit. We gave the hint to think about theFig. 2. Setup of the haptic model to teach Lookup in a binary search treesearch strategy in a dictionary. Afterwards they repeated theexercise with the corresponding Spreadsheet 2.To explain Lookup in a binary search tree and the treestructure itself we used the well-known plates and bricks. Aparent node consists of the merged two cells above the twochildren nodes. The students had to build a border structuresuch that the cells are represented by the empty fields betweenthe bricks. Additionally we also used a molecular model setto illustrate the tree structure. Afterwards the students testedthe searching strategy using Spreadsheet 3.After having executed each task, we asked the participants toexplain the algorithms with their own words, giving us furtherinsight into the individual understanding of the concepts.Apart from the three mentioned algorithms, we also in-troduced the naive string search algorithm (also called naivepattern matching) and the students were supposed to recreatethe idea on the plate. This is illustrated in Fig. 3. We did notexamine the results quantitatively for this task.C. ExperimentOur concept was tested in Rimini, Italy during a holidaycamp of young VI students in August 2012. We had 5 blindparticipants without mental disorders of ages between 17and 29 years (4 female and 1 male) with different levels ofeducation (2 high school, and 3 graduated). They also hada different employment status (2 employed, 1 students, and2 unemployed). They all had previous working experience withcomputers, but no programming experience.3 of our participants used their own computer environmentwhile the other 2 used computers provided by us. All machinesused Microsoft WindowsTM, Microsoft ExcelTMand the ScreenReader JawsTM.The experiment was conducted by one of the authors, whois visually impaired himself. He has more than ten yearsexperience in teaching VI and taught computer science andmath at a mainstream school for seven years.The course took 4.5 hours. In this time we alternatedbetween theory, playing with the toys, and practice with thecomputer as necessary and appropriate. All instructions weregiven orally. The time that each participant needed to fulfillthe exercises was measured during the course. We also wrotedown questions, comments, and the results of the exercises.169169Fig. 3. Setup of the haptic model for the naive string matching algorithmIV. RESULTSAfter executing the three algorithms in the respective hapticmodels, all participants were able to successfully solve alltasks in the spreadsheets.A. Linear search in sorted listThe times needed to execute the first task are shown inFig. 4. All participants needed roughly the same time (between2.5 and 3.5 minutes). During our observation we noted, thatnone of the participants made use of thePage down key. Thisis interesting, because the Screen Reader Jaws uses a stickykey function (Windows StickyKeys feature) for the arrow keys.Therefore, the user has to press the arrow keys once for eachrow he wants to move down, instead of keeping it pressed.B. Binary search in sorted listAll participants were able to apply the concept of the upperand lower limit which have to be moved to reduce the searchrange. In discussions we found out that they also understoodthe analogy to the searching strategy in printed dictionaries.To realize the upper and lower limit they inserted the letter xin the second column of the spreadsheet. This allowed them tonavigate comfortably between the two limits withCtrl +andCtrl + .We observed that interestingly some participants switchedfrom binary search back to linear search after the executionof the first or second step.4Participant B proposed a different strategy. He divided thelist in ten sections and assumed that the numbers are evenlydistributed (which was true in our example). Depending onthe thousands place he jumped directly to the correspondingnumerical range and used a linear search there.The times needed to execute the second task are shownin Fig. 5. This task was executed twice and the times rangebetween 17 and 35 seconds with no significant difference fromthe first to the second run. This suggests, that the participantshad already understood the algorithms after executing it in thehaptic model and before using it in the spreadsheet.3Times over one minute are rounded to multiples of 10 seconds.4Interestingly, a similar optimization for sufficiently small lists is alsoapplied in actual practical implementations of search algorithms.Fig. 4. Time in seconds to solve Linear search in sorted listFig. 5. Time in seconds to solve Binary search in sorted listFig. 6. Time in seconds to solve Lookup in binary search treeThe observation revealed a drastically reduced search timefor the binary search compared to the linear search for all par-ticipants. This served as good motivation for the participantsto occupy with algorithms and to really grasp that they canoffer a benefit in practical applications.C. Lookup in binary search treeWhen using the spreadsheet to navigate in the representationof the binary tree, we could observe that the navigation froma node to its left or right child was a notable difficulty for theparticipants. This is because when pressing , the behaviorof the spreadsheet programs is not intuitively predictable. Thetimes needed to execute the third task are shown in Fig. 6.A significant improvement in the second and third executionis visible for all participants (except for participant B, whowas already very fast in the first run). This suggests, that theparticipants improved their understanding of the data struc-ture and algorithm during the execution in the spreadsheet.Participant C needed more time and had problems with thenavigation in the spreadsheet, probably because she/he doesnot use the computer daily.170170V. DISCUSSIONIn our case study it turned out that the proposed teachingmethod is a suitable way to teach basic algorithmic thinkingto VI. After using the haptic model, all participants were ableto understand the underlying concepts and to solve the giventasks. Furthermore they were able to describe the algorithmswith their own words. The method has the advantage to inhibitthe common problem of thinking of many steps concurrently,because it forces the students to carry out the steps withtheir hands consecutively. Therefore this method is also asuitable way to teach sighted students, and can presumably besuccessfully used in inclusive classes as well. An additionaladvantage is, that the method does not need expensive orcomplicated equipment, but only relies on a few very simpletools. The spreadsheets are available online.5The major limitation of our experiment is the small sam-ple size. Fortunately, the number of VI is relatively small.Unfortunately, this imposes a major restriction to the size ofVI test groups. Therefore, the preconditions (educational andsocial backgrounds, disabilities etc.) are very different in largergroups. However, the focus of our research was to examinewhether such a teaching method is viable, and not to performan extensive quantitative analysis.We considered the opportunity to test the concept in in-clusive mainstream classes, but the presence of an additionalforeign teacher would have lead to further atypical situationsfor the included VI and we started with the idea to reducesuch situations.VI. OUTLOOKIn the future we plan to evaluate and extend this teachingmethod in a case study with more participants. We also planto adapt it to other topics, in order to give further suggestionsto teachers sharing our conviction that education should beaccessible for all.ACKNOWLEDGMENTWe would like to thank the reviewers for their commentsthat helped to improve the paper. Special thanks also to theparticipants who volunteered to attend the course, enabling usto conduct our case study.5http://www.ddi.edu.tum.de/publikationen/REFERENCES[1] UN General Assembly, Convention on the rights of persons withdisabilities: resolution / adopted by the general assembly, Jan.2007, a/RES/61/106, Accessed 22 July 2012. [Online]. Available:http://www.unhcr.org/refworld/docid/45f973632.html[2] J. M. Francioni and A. C. Smith, Computer science accessibility forstudents with visual disabilities, SIGCSE Bull., vol. 34, no. 1, pp. 9195, Feb. 2002.[3] G. Douglas, C. Corcoran, and S. Pavey, The role of the WHO ICFas a framework to interpret barriers and to inclusion: visually impairedpeoples views and experiences of personal computers, British Journalof Visual Impairment, vol. 25 no. 1, pp. 3250, 2007.[4] M. E. Califf, M. M. Goodwin, and J. 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