Orchestrating the XO computer with digital and conventional resources to teach

Orchestrating the XO computer with digital andconventional resources to teach mathematicsA. Díaz,* M. Nussbaum* & I. Varela†*Faculty of Computer Science, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile†Facultad de Ciencias Humanas, Universidad Católica de Uruguay, Montevideo, Uruguay

Abstract Recent research has suggested that simply providing each child with a computer does not leadto an improvement in learning. Given that dozens of countries across the world are purchasingcomputers for their students, we ask which elements are necessary to improve learning whenintroducing digital resources into the classroom. Understood the orchestration as the coordi-nation of conventional and digital resources, we examine whether effective resource orches-tration improves student learning. To do so, an 8-month study was undertaken across 17schools in Uruguay, involving 544 fourth grade students. A treatment group worked under anorchestrated strategy, while a control group followed the national strategy, which includes theOne Laptop per Child Computer (XO). Our study confirmed that the national strategy aloneis capable of producing significant progress in student learning. However, we also found thatthe orchestration produces a statistically significant improvement in the results when com-pared to the control group. The main finding indicates that when teachers have an orchestratedstrategy to integrate digital and non-digital resources, student learning is enhanced in relationto the curricular objectives. Finally, we discovered that learning is improved in the treatmentgroup when there is systematic use of resources, and when the school’s infrastructure facili-tates the use of the technology.

Keywords determinants of effectiveness, resource integration, resource orchestration, teachers’ work inthe classroom, teaching mathematics, XO computer.

Introduction

The One Laptop per Child initiative (Kraemer,Dedrick, & Sharma, 2009) has promoted the supply oflow-cost computers to students around the world withthe aim of improving their learning. Although over 20countries are already putting this initiative into prac-tice, considerable controversy remains as to its possibleimpact (Nugroho & Lonsdale, 2010). Independent

studies have concluded that providing each child with acomputer does not lead to improvements in learningmathematics or reading (Cristia, Ibarrarán, Cueto,Santiago, & Severín, 2012).

One of the countries to join this initiative wasUruguay, following a countrywide policy implementedin 2009 to provide every primary school student withan XO computer (Collaborating Wikipedia, 2012). Oneof the initiative’s objectives is to improve teaching andlearning processes and increase motivation in order toconnect with student and teacher knowledge (Flores &Hourcade, 2009). In order to achieve this objective, andto make the best use of the computers, enoughresources must be made available to ensure that they

Accepted: 05 July 2014Correspondence: Anita Díaz, Faculty of Computer Science,Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860,Macul, Chile. Email: [email protected]

bs_bs_banner

doi: 10.1111/jcal.12081

Original article

© 2014 John Wiley & Sons Ltd Journal of Computer Assisted Learning (2015), 31, 202–219202

mailto:[email protected]

are adequately maintained and that the relevant contentand services are accessible.

Sharples (2013) reviews the relationship betweenresources and the interaction between individuals in aclassroom. He claims that the classroom has become amore complex and demanding place, in which theteacher no longer merely plans a lesson, adapts to offi-cial programs and takes care of safety and discipline,but must now also understand and manage a variety oftechnologies. As the teacher must not only learn andmanage a new form of technology, but also promotedirect interaction between themselves and the students,between the technology and the students, and amongthe students themselves, supporting the teacher andproviding them with a set of guidelines is essential(Hämäläinen & Oksanen, 2012). This set of guidelinesis known as orchestration.

Orchestration provides an innovative robustness toteaching and learning, while taking classroom ecologyinto account; efficiency, as it enables the use of triedand tested resources; adoptability, as it facilitates theteacher’s organization of relevant resources; and adapt-ability, as teachers are equipped with strategies foradjusting the resources according to possible outcomes(Roschelle, Dimitriadis, & Hoppe, 2013). In addition tothis, Mercer and Littleton (2007) highlight the possibil-ity of the teacher generating an environment conduciveto productive discussion and a sense of mediation withinthe learning process. This allows for concentration onhigher order issues, leaving lower order issues (such asthe application and combination of basic solutions usingInformation and communications technology (ICT)resources) at the mercy of an orchestration model.

However, the presence of multiple forms of informa-tion and communication, with and without ICTresources inside the classroom, does not guarantee animprovement in student learning. It must be accompa-nied by the pedagogical orchestration of the resourcesinvolved (Prieto, Villagrá, Jorrín, Martínez, &Dimitriadis, 2011). This gives rise to the first researchquestion, which seeks to evaluate the effect of an ICTintegration model with other orchestrated learningresources on the work of a teacher: Does student learn-ing improve when teachers are provided with a varietyof conventional and digital resources that form a coher-ent orchestrated unit?

In order to analyse the findings from the first ques-tion in greater depth, and get a better understanding of

the environment when the learning achievements aresimilar or different among various groups of students,we asked a second research question: Which are theelements that improve learning when introducingdigital resources into the classroom?

The following sections present the interventionmodel, where the treatment and pedagogical strategyimplemented are explained in detail; the experimentaldesign, which explains the sample and data collection;the research results, which explain the obtained find-ings through different statistical techniques; and theconclusions, which discuss the lessons drawn from thisexperience.

Intervention model for treatment group

The content developed throughout the 8-month studyfocused on mathematics and in particular the followingconcepts: place value, division, fractions, decimals andgeometry, all at fourth grade level. These math topicsare documented in Appendix I. Within this curricularframework, the goal of the in-class research was, on theone hand, to practice the standard procedure, and onthe other, to experiment with different representationsof the math topic, helping the student to integrate thetwo ways of looking at an operation. The interventionwas framed by three components.

Teaching strategy orchestrations and resources

The pedagogical proposal was based on the integrationof digital and non-digital resources, supported by theorchestration of resource integration in the classroomand serving as a guide for the teacher. This design wasstructured to answer the paper’s first research question:Does student learning improve when teachers are pro-vided with a variety of conventional and digitalresources that form a coherent, orchestrated unit? Dif-ferent learning modules were developed for each of themath topics included in the study. Each of thesemodules consisted of a variety of teaching resourcesthat could be used together to achieve a given objective,along with an orchestration to guide the teacher onassociated logistical and pedagogical aspects.

The orchestration booklet was designed based on themodel described by Nussbaum and Díaz (2013) andconsists of logistical and pedagogical guidelines forintegrating and implementing digital and non-digital

Orchestrating the XO computer 203

© 2014 John Wiley & Sons Ltd

resources that aim to achieve the same teaching objec-tive. The orchestration booklet included a guide on howto approach each math topic from both a logistical andpedagogical point of view. In terms of logistics, thisincluded suggestions on how to manage the time, spaceand resources to enhance the learning experience. Inpedagogical terms, it included guidance on how theteacher should interact with the students when acquir-ing the new knowledge (key questions, examples, etc.).The resources included in the orchestration for each ofthe math topics are listed and described below.

Material for exploring and discovering contentPowerPoint (PPT) presentations to introduce thecontent to students in a methodologically sound andinteractive way, placing new concepts within a narra-tive that significantly improves student comprehension(Burton, 2002). Figure 1 shows an example of how amath topic is taught using an everyday situation, wheretwo children are presented with a challenge they mustsolve. This must be done as a group, mediated by theteacher, and using the new procedures that have beenstudied for ‘Calculating using rational numbers (frac-tions)’ (Appendix I, Topic 6).

Personal interactive activityDigital content for personal practice where the studentswork at their own pace. For the digital work, the XOcomputer acted as a mediator allowing the teacher tochoose between two different open source activities foreach math topic. In general, the available open sourceactivities lacked an adequate narrative and feedback, aswell as containing graphics that were inconsistent withthe rest of the intervention’s educational resources.

Personal activities using paper-based exercisesNon-digital work where worksheets (WS) were pro-posed to develop argumentation and procedural skillsfor mathematics. Students were encouraged to investi-gate and practice using different methods to solve theexercises relating to the new concept.

Transferring content to new situationsGame-based educational resources in which studentswere able to establish connections with prior learningby using PPT, XO and study guides. For example,Figure 2 shows a game-based activity (GBA) in whichthe children worked in groups of three and had to apply

the new math topics in order to complete the game. Inthis case, the teacher mediated the interactions betweenthe groups, ensuring that the challenges were correctlysolved by the students. In some cases, the materialsused in the activities included cut-outs to use asmarkers or pieces for the game.

Appendix II outlines an example which is theorchestration of Class 1 on the math topic of converting

Figure 1 Sample Narrative in PPT

A. Díaz et al.204


fractions to decimals, focusing on exploring contentthrough the use of PPT and strategies to activate priorknowledge. The rest of the orchestration for this topic,not shown in Appendix II due to space restrictions,provides the teacher with instructions on how to inte-grate and adjust various learning experiences in orderto teach the same math topic by making use of differentresources.

Some of the participating teachers felt that the pro-posed materials needed to be adapted to the differentrealities and requirements of each school. Theseminor changes only altered the order of the orches-tration by less than 5% and we can therefore say that,in general, all of the schools followed the sameorchestration.

These changes are not considered as bias as theorchestration booklet was designed with timings whichwould allow for minor changes, bearing in mind theflexibility which Dillenbourg, Nussbaum, Dimitriadis,and Roschelle (2013) mention with regard to orches-tration. The documented changes that were made to theorchestration booklet were the following:

• Allocated time for each math topic: rather thanworking on one math topic per week, the teachersextended the time spent on each concept according tothe needs of their students, spending up to three timesthe original allocated time on a concept.

• Order of the math topics, in line with the school’sown pre-established plans.

• Removing a math topic. Only one school removed aspecific concept from the program (‘Locating deci-mals on number lines’ – Appendix I, Topic 10).

• Activities based on transferring content to new situa-tions; certain GBAs were not included due to a lack oftime.

• PPT presentation subject to the availability of a pro-jector. Where schools did not have access to a projec-tor, the presentation was either shown to the wholeclass on a single netbook or desktop, or on each XO,where the students followed the teacher’s instructionsto work through the presentation. In these cases, theschools did not follow the recommendations set out inthe orchestration booklet on how to work with PPTpresentations, where the suggestion was to project thePPT to the whole class so that every child could see it.

• Two children working on the same XO computer dueto a lack of working computers in some schools. Inthese cases, the XO method suggested by the orches-tration booklet was modified as it recommendedhaving each child work individually, in line with thegovernment initiative that refers specifically to 1:1work.

• Incorporating the school’s own strategy and resourcesinto the proposal.

Teacher training and coaching

The training process was designed based on the char-acteristics of the teaching strategy. This process, aimed

Figure 2 Sample Game



at teachers from all of the schools in the treatmentgroup, consisted of four sessions to present and explainthe pedagogical proposal. The sessions took place inthe schools and were attended by the participatingteacher, head teacher and coordinating teacher. Eachsession lasted 1.5 h and encouraged the teachers toreflect on the components of the teaching strategy. Theobjectives for each session were as follows: initialsession, to understand the objectives of the study andhow it would be implemented; Session 1, to introducethe strategy to be used in the classroom through apractical demonstration; Session 2, to plan the logisticsof implementing the strategy in the classroom; Session3, to introduce student tracking and supportprocedures.

Tracking implementation in schools

The tracking process was developed in order to collectinformation that would help answer the secondresearch question, which aims to identify whichelements improve learning when introducing digitalresources into the classroom. The tracking was com-posed of three stages: logbooks, classroom observa-tions and meetings.

LogbooksThe orchestrations were developed so as to allow theteachers to adapt them to their specific needs. In orderto understand how these orchestrations were imple-mented by each teacher and to analyse the effects of thesystematic use of each material on the progressreported by students, the teachers kept a logbook thatdetailed the use or non-use of resources for eachsession and the decisions taken regarding their work inthe classroom (the results are documented in detail inTable 1 and summarized in Table 2). Therefore, thesystematic use of the teaching strategy’s materials wasthe first level of variables which were analysed in orderto answer question 2. These variables correspond toeach of the orchestration’s materials, and for each mathtopic: use of PPT; use of XO computer; use of WS; anduse of GBA. Descriptive statistics were used to calcu-late the averages of the various integrated resourcesand to produce an index for each of these (Table 2).

ObservationsThe teacher support process consisted of individualcoaching by the field researchers for each kind of class-

room task: exploring content, practising the contentand transferring the content to new situations. Obser-vations were the second level of variables that wererecorded in order to answer question 2, and evaluatewhether these components were one of the possibledeterminants of progress in student learning. At the endof the class, the field researcher provided the teacherwith feedback regarding the classroom work and thevarious observed components. The results of theseobservations were systematized using descriptive sta-tistics (Table 3), in which each of the elements thatwere observed was considered a variable, taking as itsvalue the average performance index from the threeobservations carried out with each teacher for each ofthe evaluated components. These were class structure,use of planning, content explanation, feedback pro-vided to the children, resource integration and groupmanagement.

MeetingsOccasions where the field researcher advised theschool team on the systematic nature of the implemen-tation and the integration of resources, analysing pos-sible curricular flexibility according to the particularneeds of the teachers. During these meetings, the fieldresearchers recorded context variables in order toanalyse the possible effect that each of these elementscould have on the students’ learning outcomes. Fourvariables were recorded for each class: whether or notthere was Internet, a projector, and a 1:1 computer tostudent ratio (1 XO per child), as well as the class size(Table 4). These recordings were the third level of vari-ables to be analysed.

Experimental design

Sample

To answer the two aforementioned research questions,an exploratory study was undertaken using a quasi-experimental design and applied to 17 public schools inurban Montevideo, Uruguay. Among these 17 schools,10 classes from 7 schools were identified as the treat-ment group and 10 classes from 10 schools as thecontrol group. All students were evaluated by applyinga pre- and post-test at the beginning and end of theresearch. Each group was composed of fourth gradestudents and their respective mathematics teacher.

A. Díaz et al.206


Table 1. Logbook Record for Systematic Use of Resources for Each of the Treatment Group Classes

Class 1

Resource T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 M Mean %

PPT 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0.79 79XO 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0.74 74WS 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0.68 68RA 1 0 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0.63 63

Class 2

PPT 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 1 0.79 79XO 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0.79 79WS 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 0.84 84RA 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 0.74 74

Class 3

PPT 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0.79 79XO 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0.79 79WS 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0.84 84RA 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 0.79 79

Class 4

PPT 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0.37 37XO 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0.21 21WS 0 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 0.63 63RA 0 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0.58 58

Class 5

PPT 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0.58 58XO 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0.21 21WS 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 0 0 0 0.63 63RA 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0.68 68

Class 6

PPT 1 1 0 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0.68 68XO 1 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 0.47 47WS 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0.84 84RA 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0.74 74

Class 7

PPT 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0.68 68XO 1 1 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 0 0 0.53 53WS 1 1 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0.58 58RA 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0.37 37

Class 8

PPT 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.00 100XO 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0.68 68WS 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.95 95RA 1 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0.58 58

Class 9

PPT 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 1 1 0.79 79XO 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0.63 63WS 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0.84 84RA 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0.37 37

Class 10

PPT 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0.89 89XO 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0.79 79WS 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0.89 89RA 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0.84 84

1 = use of resource in math topic (T). 0 = no use of resource in math topic (T); PPT = PowerPoint; XO = digital activities on one netbook per child; WS = worksheet;RA = game-based activity; T(1–19) = math topic; M = average use of each resource; Mean % = average use of each resource by percentage.



The sample initially consisted of 544 students (257treatment group students and 287 control group stu-dents). However, by counting only those students whoparticipated in both the pre- and post-tests, the finalsample was 400 students (192 treatment students and208 control group students). This loss of data was dueto students being absent on the day of the post-test.Table 5 shows the characteristic elements of theprocess for the control group and the treatment group.It was decided not to do classroom observations nortracking and monitoring with the control group so asnot to interfere and avoid any type of bias which couldalter the schools’ natural course.

Instruments

Measuring learningThe quantitative evaluation was undertaken using apaper-based test for both the pre- and post-tests. Itconsisted of 38 questions, resulting in an exhaustiveassessment of math topics covered during the study.

The time limit for completing each test was 75 min.The test scored a Cronbach’s alpha (Cronbach, 1951)of 0.82 for the post-test, which was taken by 400 stu-dents in total. To support this test, an application proto-col was established in order to ensure consistencyamong the interaction between the evaluators and thechildren.

Classroom observationAs it was explained in the section ‘Tracking implemen-tation in schools’, teacher performance was evaluatedthree times, with each observation lasting between 45and 60 min, corresponding to the time spent on math-ematics each day.

Six components were evaluated during each obser-vation: the structure of the class, the use of planning,the integration of resources, content explanation, feed-back provided to students and group management.Each of these components was measured on a scale of1–4 using an observation form, in which 1 is the lowestscore and 4 is the highest. A member of the research

Table 2. Average Use of Each Resource byPercentagePowerPoint

(%)XO(%)

Worksheet(%)

Game-basedactivity (%)

Class 1 79 74 68 63Class 2 79 79 84 74Class 3 79 79 84 79Class 4 37 21 63 58Class 5 58 21 63 68Class 6 68 47 84 74Class 7 68 53 58 37Class 8 100 68 95 58Class 9 79 63 84 37Class 10 100 89 100 95

Table 3. Average Teacher Performance Index by Each Variable Observed

Variables

Treatment group

Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9 Class 10

Class structure 4.00 1.67 2.00 2.67 3.00 3.33 3.33 3.00 2.67 3.67Use of planning 3.67 1.67 2.00 2.33 2.33 2.33 3.00 3.50 3.00 3.00Resource integration 3.67 2.00 2.33 3.00 2.67 2.67 4.00 3.50 3.00 4.00Content explanation 4.00 2.33 3.33 2.67 2.67 3.00 3.33 3.50 3.33 4.00Feedback provided

to students4.00 2.67 3.67 2.67 2.67 2.67 4.00 2.50 4.00 4.00

Group management 4.00 3.00 4.00 3.67 3.67 3.33 4.00 3.00 3.67 4.00

1.00–1.99 = insufficient performance observed; 2.00–2.99 = regular performance observed; 3.00–3.99 = good performanceobserved; 4.00 = optimum performance observed.

A. Díaz et al.208


team applied this form to evaluate teacher performancein the classroom once the class had finished. Theresearchers noted all the information necessary for theassessment (Appendix III), in line with previouslyestablished criteria (Appendix IV).

The observation guidelines were developed by con-sidering the knowledge and skills that are necessary fortechnology to be adequately integrated into the class-room. Hogan (2007) argues that it is possible to evalu-ate specific tasks using an instrument consisting of theskills that are necessary for performing an action, skillsthat are expressed in the observation criteria rubric(Appendix IV).

Results

Analysis of the treatment and control groups’ pre-and post-test scores

The study looked into the significance of the differencebetween the pre- and post-test scores for each student.The analysis for both groups was done independently,taking into account only those students who partici-pated in both the pre- and post-tests. Both the controlgroup and the treatment group showed statistically sig-nificant differences at 99% between their pre- and post-test scores (Table 6). However, the control groupshowed a medium effect size (Cohen’s d 0.41) whilethe treatment showed a large effect size (Cohen’s d1.04). This was calculated using descriptive statistics,specifically the mean and standard deviation for eachgroup and for each measurement (Table 6), so as toidentify the effect size (Cohen’s d) of the teachingstrategy on student learning when compared with thecontrol group. It should be noted that the outliers wereeliminated for this analysis because they could haveinfluenced the results in the differences studied. Thir-teen cases, all high, were removed: eight from thetreatment group and five from the control group.

To evaluate the differences in the progress notedin the students from the control group and from thetreatment group, the score on the initial measurement

Table 4. Register of Contextual Characteristics

ReliableInternet

Projectoravailability

Equipmentratio 1:1

Classsize

Class 1 1 1 1 28Class 2 1 0 0 22Class 3 1 0 0 22Class 4 1 1 0 13Class 5 1 1 0 14Class 6 1 1 0 18Class 7 1 1 0 19Class 8 0 0 1 13Class 9 0 0 1 21Class 10 1 0 1 22

1 = condition present; 0 = condition absent.

Table 5. Sample Design

Group numberNo. ofschools

No. ofclasses

No. ofstudents Characteristic elements

Control 10 10 208 Measuring learning (details Instruments)Use of XO computer per child

Treatment 7 10 192 Measuring learning (details Instruments)Classroom observation (details Tracking implementation in school)Teacher training and coaching (details Teaching training and coaching)Orchestrations and resources (details Teaching strategy orchestration

and resource)Tracking and monitoring (details Tracking implementation in school)Use of XO computer per child (details introduction)

Table 6. Pre- and Post-Test Results perGroup

Group N

Prea Posta

p Cohen’s dX SD X SD

Control 203 9.91 3.70 11.46 4.68 0.00*** 0.41Treatment 184 11.61 5.44 17.73 6.98 0.00*** 1.04

aThere was a total of 38 points available on the test.***p = 0–0.001; **p = 0.001–0.01; *p = 0.01–0.05; °p = 0.05–0.1.



was controlled. As shown in Table 6, the average forthe control group in this measurement was lower thanfor the treatment group. In order to do this, an analysisof covariance (ANCOVA) was conducted, which is aprocedure that eliminates heterogeneity between thetreatment groups, relating to characteristics not con-trolled for in the experimental design. In this case,heterogeneity in the initial learning scores reported byboth groups was eliminated, separating out any predic-tions based on initial student scores in favour of thefinal progress results.

Table 7 shows that the pedagogical strategyemployed has a significant effect (p = 0.01) on studentlearning. Even though the initial measurement has asignificant effect on progress, this association isinverse, because those subjects who had a higher scoreon the initial measurement showed lower progressoverall (p = 0.00). In other words, considering that thestudents experienced a gain in learning between thepre- and post-tests, each additional point on the pre-testrepresents an average effect of −0.47 on the final gain.

To confirm the association between the initial scoreand the progress that the students achieved, the data

were submitted to a correlation analysis through aPearson test. This test revealed significant results(p = 0.00) in regard to the previously mentioned nega-tive linear relationship, thus corroborating the findingthat the subjects who scored highest at the initial meas-urement also tended to make less progress in terms oflearning achievements, and vice versa, thus favouringthe students with lower performance. This correlationis further supported by taking a more in-depth look atthe scores by the highest and lowest performing stu-dents on the pre-test. To analyse the results for studentlearning in greater depth and understand the increase inthe post-intervention standard deviation, Table 8 showsthe scores for the bottom 10% of students on the pre-test and the top 10% of students on the same test, bothfor the control group and for the treatment group. Inboth groups (control and treatment), on average thelowest performing students on the pre-test see a greaterincrease in their scores than the top performing stu-dents on the pre-test (4.59 vs. −0.54 for the controlgroup and 7.40 vs. 2.46 for the treatment group).

This is supported graphically by the histogram inFigure 3, which shows the distribution of scores bychildren from both groups on each of the tests. Inparticular, as well as observing an improvement forboth groups on the post-test (the data reveal an increasein the median score), an increase in the dispersion ofthe data can also be observed both in terms of scores aswell as frequency. In other words, there are higherscores on the post-test than on the pre-test and, at thesame time, a lower concentration within a set range ofscores (the frequency of students with those scores islower, leading to greater variability).

Table 7. Analysis of Covariance

Estimate SD t value p (>|t|)

Intercept 0.17 0.03 6.12 0.00***Pedagogical strategy 0.09 0.04 2.53 0.01*Initial measurement −0.47 0.10 −4.88 0.00***Treatment: initial

measurement0.16 0.12 1.36 0.18

***p = 0–0.001; **p = 0.001–0.01; *p = 0.01–0.05;°p = 0.05–0.1.

Table 8. Breakdown of Results on the Pre-Test

Group

Prea Posta

Improvement XX Min Max X Min Max

Control Entire group 9.91 1.83 26.00 11.46 1.00b 26.00 1.55Control Top 10% pre-test 17.03 14.50 26.00 16.48 6.00 26.00 −0.54Control Bottom 10% pre-test 3.63 1.83 4.83 8.23 2.17b 6.17 4.59Treatment Entire group 6.61 0.00 29.33 17.73 3.17 33.67c 6.12Treatment Top 10% pre-test 21.83 19.33 29.33 24.29 13.17 33.00c 2.46Treatment Bottom 10% pre-test 3.07 0.00 4.50 10.46 3.17 20.33 7.40

aThere was a total of 38 points available on the test. bThe minimum scores are not the same because the ‘entire group’ includes allthe children from this group, while the statistics for the bottom 10% only include the children who scored in the bottom 10% onthe pre-test. cThe maximum scores are not the same because the ‘entire group’ includes all the children from this group, while thestatistics for the top 10% only include the children who scored in the top 10% on the pre-test.

A. Díaz et al.210


This can be explained as the effect of the pedagogi-cal strategy on the presence or absence of previouslearning. Those children who scored higher in theinitial measurement could have received previous train-ing associated with the math topics covered by themeasurement instrument, while those who scored lowmight not. Thus, we may conclude that the presence ofthe strategy would have the effect of reinforcing thelessons for those children with previous studies, butwould act as an introduction and transfer of informa-tion for those children who initially started withoutthese studies.

Treatment group analysis

In order to discover student learning determinantswithin the treatment group (Keppel & Wickens, 2004),all of the comparisons between treatment classes weredone in pairs using the Bonferroni correction. Signifi-cant differences were observed between Classes 5 and

10 with Classes 1, 2, 3, 7 and 9; between Class 8 andClasses 2, 3, 7 and 9; and between Classes 4 and 6 withClasses 2, 3 and 9 (Table 9).

Using the data provided by Table 9, a multi-levelanalysis model has been developed for the treatmentgroup. An analysis of the nested data was carried out inorder to explore which contextual and methodologicalvariables involved in the pedagogical strategy impacted– to a greater or lesser degree – learning among chil-dren in this group. The dependent variable was eachstudent’s progress and the contextual variables evalu-ated refer to three areas, which have had trackingduring the intervention:

• The systematic use of the teaching strategy materials,with results documented in Table 2, and furtherdetailed in Table 1.

• Teacher performance index, following observationsthroughout the study, with results documented inTable 3.

Figure 3 Histogram



• Contextual characteristics for each group of students,with results documented in Table 4.

The control group has not been included in theHLMs as this analysis included variables that wererecorded during tracking and classroom observation.As was explained in the section ‘Sample’, it wasdecided not to do tracking or classroom observationswith the control group so as not to interfere and avoidany bias which could alter the schools’ natural course.Given this decision, class-level variables are not avail-able for the control group, and therefore these studentscannot be included in the analysis. In any case, thisdoes not affect our second research question.

In order to examine the existence of a possible asso-ciation between the characteristics of each class and theachievement of each child, the group of treatmentclasses was analysed for intra-class correlation. Thefindings indicated that 11% of the variance in progressachieved by all the students in the treatment group iseffectively due to the differences between classes.Thus, it should be noted that 89% of the variance is dueto other differentiating elements in each classroomwhich have not been included in this study, as well asindividual characteristics such as previous learningexperiences on the math topic, family socio-economiclevel and class attendance. Table 10 depicts the threemodels analysed using this statistical technique.

Model 1 examines the category systematic use ofresources for student learning. It is observed that bothgreater use of PPT presentations for introducingmaterial with a narrative and use of GBAs that incor-porate math topics and operations have a marginal butpositive association with student learning. However,greater use of XO contents is negatively associated

with progress in the children’s learning. These resultscast doubt on the current use of XO digital content thatis accessible on the Internet, since some of the charac-teristics of this content may construct incorrectconceptualizations for mathematical learning. Beyondthe individual significance of each variable, Model 1 isstatistically significant as a set (p = 0.01).

Model 2 shows the association between the variablesfor the category of teacher performance with the chi-ldren’s final progress in their learning. In this category,the only variable shown to be predictive is feedback forthe students. This prediction is inversely linear. Thus,when the teachers gave more feedback to their stu-dents, those students showed less progress in theirlearning. This result could be studied in further detailby analysing the type of feedback given and the con-tents of the interventions made by the teachers whenworking with the students. No other variable in thiscategory suggests an association with student progress.In this case, Model 2 is not statistically significant(p = 0.12).

Finally, Model 3 evaluates the association of Con-textual characteristics variables for each class, demon-strating that there is an inverse effect between class sizeand progress in student learning. Thus, the studentswho showed the most progress were those whobelonged to the smaller groups. On the other hand, thevariables of Internet reliability and 1:1 ratios are posi-tively associated with student learning. This couldsuggest the importance of internal administration ateach educational establishment. Even though the Uru-guayan government has implemented a public policyfor providing Internet connectivity for Montevideo(and other cities), the reliability of this service dependson the management of individual resources within each

Table 9. Pairwise Comparisons Using t-Tests With Pooled SD

MClass 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 90.15 0.07 0.11 0.22 0.25 0.20 0.12 0.22 0.11

Class 2 0.07 0.07° – – – – – – – –Class 3 0.11 0.31 0.46 – – – – – – –Class 4 0.22 0.29 0.02* 0.09° – – – – – –Class 5 0.25 0.05* 0.00*** 0.01* 0.61 – – – – –Class 6 0.20 0.27 0.01* 0.05* 0.84 0.39 – – – –Class 7 0.12 0.51 0.32 0.77 0.14 0.02* 0.11 – – –Class 8 0.22 0.22 0.01 0.05* 0.97 0.61 0.78 0.09 – –Class 9 0.11 0.36 0.44 0.95 0.10 0.01* 0.06° 0.82 0.06° –Class 10 0.24 0.04* 0.00*** 0.00** 0.67 0.88 0.41 0.01* 0.67 0.01*

***p = 0–0.001; **p = 0.001–0.01; *p = 0.01–0.05; °p = 0.05–0.1.

A. Díaz et al.212


establishment. Likewise, even if 1:1 ratio is a policyimplemented on a national level, it should be noted thatnot all of the schools and classes maintain all of theirequipment in working order. Maintenance of the equip-ment varies between establishments depending on theirinternal administration and specific context, as well asthe level of communication between each establish-ment and the technical support team provided byCeibal to assist in this matter. This final model, Model3, is statistically significant when all of the variablesare analysed as a set (p = 0.00).

Conclusion and discussion

In a context in which the national strategy includes theuse of XO computers that can independently producesignificant improvements in student learning, theresults of this study show that the studied interventionproduced statistically significant results when com-pared with a control group. The results of this paper arecoherent with those put forward by Luckin et al. (2012)which reveal the importance of avoiding the assump-tion that technologies in themselves produce learning,and do so effortlessly, given that the children in bothgroups, treatment and control, had one computer perchild.

The first research question ‘Does student learningimprove when teachers are provided with a variety ofconventional and digital resources that form a coherentorchestrated unit?’ is answered by the results displayedin Table 7 by comparing learning achievement betweenthe control and treatment groups. This shows thatstudent learning (in terms of the curricular teachingobjectives) improved in proportion to their teachershaving access to an orchestration strategy for digitaland non-digital resource integration. This confirms theimportance of continuing to develop resource integra-tion strategies that clarify for the teacher the pedagogi-cal aim of the resources used (digital and non-digital)and the desired learning outcome. The value of tech-nology does not lie within the technology itself, butwill rather come from the relationship that it has withthe other learning experiences that the students developin the classroom. This highlights the value of orches-tration, which coherently lays out the logistical andteaching components required in a classroom whenintegrating technology and other tools to promotelearning.

Regarding the second research question, ‘Which arethe elements that improve learning when introducingdigital resources into the classroom?’, we discoveredthat learning is improved when there is systematic use

Table 10. Coefficients From the Hierarchical Linear Model Analysis of Final Student Achievement

Variable

Model 1 Model 2 Model 3

Estimated SE Estimated SE Estimated SE

Class-level equation: effects on adjusted mean achievement between classesIntercept −0.07 0.10 −0.05 0.21 0.30 0.06***Systematic use of resources

PowerPoint 0.40 0.21°XO contents −0.44 0.13***Worksheet 0.12 0.23Game-based activity 0.16 0.10°

Teacher performanceClass structure 0.03 0.05Use of planning −0.03 0.07Resource integration 0.04 0.06Explanation of the contents 0.06 0.07Feedback for the students −0.12 0.06***Group management 0.09 0.09

Contextual characteristics for each classClass size −0.02 0.00***Internet reliability 0.15 0.05***Availability of a projector 0.01 0.031:1 ratio 0.15 0.03***

***p = 0–0.001; **p = 0.001–0.01; *p = 0.01–0.05; °p = 0.05–0.1.



of resources, and when the school’s infrastructurefacilitates the use of the technology. Furthermore, ourexperiments show that when teachers have an orches-trated strategy to integrate digital and non-digitalresources, student learning is enhanced in relation tothe curricular learning objectives.

A detailed analysis of the different classes compris-ing the treatment group shows the direct effect of thesystematic use of the teaching strategy’s variousresources, Model 1 (Table 10). Quantitative analysisreveals that using PPT has a positive effect on studentlearning, allowing new contents to be explored throughgraphic and contextual narratives. This is also the casewith the GBAs, through which children interacted withthe new content in groups and out of self-interest.However, it is interesting to analyse the negative effectthat increased XO use is shown to have on studentlearning. As was signalled in the section ‘Interventionmodel for treatment group’, the digital content sug-gested for working with this tool was openly availableon the Internet. This content was characterized by alack of informative feedback and, as it was web-basedcontent, its use was directly dependent on the reliabilityof the school’s Internet connection. In general, theschools’ Internet connections were adequate, althoughin a few cases the connection was intermittent, and intwo particular cases the connection was completelyunreliable (Table 4). After taking into account all of theconsidered resources, a significant effect on studentlearning is observed. Therefore, a negative effect of thecomputer itself cannot be established, with a hypoth-esis put forward instead regarding the quality of thecontent on the XO computer.

The effect of the XO computer within an orches-trated teaching strategy, controlling the quality of thedigital content, will be left as a topic for futureresearch. Accordingly, NESTA (Luckin et al., 2012)points to technological elements that could supportteaching practices and impact learning, making directreference to the use of technology more than the tech-nology itself. These elements in the context of ourstudy of the orchestrated use of the XOs include thefollowing.

Learning from experts

The orchestrations conceptualize expert experience inthe classroom and suggest possible interactions for

teachers to have with their students when working withtechnology, and help transform the experience ofworking with software into a learning experience.

Learning with others

NESTA (Luckin et al., 2012) suggests this elementbased on the collaborative or collective experienceswhich educational software can provide, highlightinglearning from peer interaction. Our study did notinclude collaborative software. However, the orchestra-tion included WS and GBAs that allowed for groupwork.

Learning through making

Students play an active and leading role in their learn-ing experiences with the software, without having todepend on external elements. As mentioned in thesection ‘Intervention model for treatment group’, theavailable digital content was not always adequate, as itlacked interactive elements within its exercises, such asfeedback and the absence of a narrative in the math-ematics problems. Such factors may have contributedto greater levels of passivity and less student involve-ment in the learning experience.

Learning through exploring

Relevant when student devices are connected to theInternet. In this case, the orchestration provides guid-ance on student exploration, as open navigation canbecome overwhelming and even detrimental to achild’s learning experience.

Learning through inquiry

The available digital content in this intervention did notallow this. However, the orchestrations during the WSand GBAs favoured this aspect by setting out challeng-ing scenarios that must be solved in order to progress tothe following activities.

Learning through practising

Technology offers a privileged space that must beadequately orchestrated with other class activities.

A. Díaz et al.214


Learning from assessment

Feedback is important because it allows the children tobecome aware of their own achievements and weak-nesses. The orchestration proposes formative evalu-ation strategies in groups, but in this situation it is thesoftware that should provide individual feedback. Inour study, the available digital content generally did notalways include such feedback.

Learning in and across settings

The digital tool in this intervention did not contribute tothis element. However, the orchestration of resourceswith different content representations (mathematicsproblems in narrative form with specific material andwith arithmetic tasks) did achieve this kind of learningand contributed to a deeper level of understanding bythe students.

At the same time, our first research question isanswered by analysing the teacher performance indicesas determinants of the children’s achievements, Model2 (Table 10). In this model, the only variable that wasshown to be significant is the feedback given by theteachers to the children while they are working. Thisrelationship is shown to be negative. Thus, when theteachers were seen to be giving more feedback to theirstudents, those students showed less progress in theirlearning. This variable could be biased as a result of thenew dynamics integrated into their teaching practices,such as the use of PPT and the work done with GBAs.Based on the information obtained by analysing theteacher performance variables, an interesting area forfuture research would be to further investigate teacher-associated variables, not only in terms of their perfor-mance in the classroom but also their beliefs, years ofexperience, relationship to technology, etc., elementsthat could reveal instructional patterns in the teachingof mathematics, either in explanations or feedback.

To expand on the answer to our first research ques-tion, the contextual elements of each participatingschool were also evaluated, Model 3 (Table 10). Theevaluation of these variables (especially the infrastruc-ture) revealed the following as determinants of success-ful student learning: a reliable Internet connection anda good student to XO computer ratio. In contextualterms, the group size of each class was also evaluated,establishing significant associations between small

groups of students and greater individual learningachievements. This is consistent with the literature(Brühwiler & Blatchford, 2011; Finn & Achilles, 1999;Hattie, 2005; Shin & Raudenbush, 2011). It would alsobe interesting to investigate whether these contextualelements may be directly associated to each establis-hment’s administrative procedures, as the literaturepoints to the effects (both direct and indirect) of lead-ership on student learning. It is estimated that 25% ofchild learning could stem from the impact of leadershipwithin the school (intra-school factors) (Leithwood,Seashore, Anderson, & Wahistrom, 2004). In futurework, it could be relevant to consider other contextualelements, including, for example, the alignment ofteacher and administration goals, administration expec-tations regarding teacher quality, organizational struc-ture and communication channels with the community,professional support and monitoring for teachers pro-vided by the administrative team.

From the results of this study, it is possible to con-clude that a coherent structure that integrates digitaland non-digital resources positively affects learning inchildren. In this way, the results indicated that provid-ing teachers with an orchestration, in which the man-agement of logistical and teaching elements within theclassroom is explicitly outlined, promoted a positivedevelopment of activities aimed at achieving curricularlearning. It would be interesting to explore the impactof a teaching strategy, such as the one evaluated in theMontevideo schools, in an experimental context, thatis, using a representative sample of schools from apredetermined population, with the intervention thenrandomly assigned to some schools. In conjunction, itwould also be interesting to consider not just theschools’ characteristics, but also the characteristics ofeach individual student in order to establish far moresubstantive conclusions. This would allow the genera-tion of greater precedents which could contribute to thepolicies of those countries that are currently supplyingtheir schools with netbooks.

Acknowledgements

The research was supported by the Center for Researchon Educational Policy and Practice, Grant CIE01-CONICYT, from Chile, Proyecto Ceibal of the Gov-ernment of Uruguay and Universidad Católica deUruguay.



References

Brühwiler, C., & Blatchford, P. (2011). Effects of class sizeand adaptive teaching competency on classroom processesand academic outcome. Learning and Instruction, 21(1),95–108.

Burton, L. (2002). Children’s mathematical narratives aslearning stories. European Early Childhood EducationResearch Journal, 10(2), 5–18.

Collaborating Wikipedia. (2012). Wikipedia OLPC XO-1.(L. e. Wikipedia, Editor) Retrieved January 7, 2013,Retrived from http://en.wikipedia.org/wiki/OLPC_XO-1

Cristia, J., Ibarrarán, P., Cueto, S., Santiago, A., & Severín, E.(2012). Technology and Child Development: Evidencefrom the One Laptop per Child Program. Department ofResearch and Chief Economist. Inter-American Develop-ment Bank.

Cronbach, L. (1951). Coefficient alpha and the internal struc-ture of tests. Psychometrika, 16(3), 297–334.

Dillenbourg, P., Nussbaum, M., Dimitriadis Y., & Roschelle,J. (2013) Guest Editors. Classroom Orchestration, Com-puters & Education, (69) 485–523.

Finn, J., & Achilles, C. (1999). Tennessee’s class size study:Findings, implications, misconceptions. EducationalEvaluation and Policy Analysis, 21(2), 97–109.

Flores, P., & Hourcade, J. P. (2009). UNDER DEVELOP-MENT: One year of experiences with XO laptops inUruguay. Interactions, 16(4), 52–55.

Hattie, J. (2005). The paradox of reducing class size andimproving learning outcomes. International Journal ofEducational Research, 43(6), 387–425.

Hämäläinen, R., & Oksanen, K. (2012). Challenge of sup-porting vocational learning: Empowering collaboration ina scripted 3D game – How does teachers’ real-time orches-tration make a difference? Computers & Education, 59(2),281–293.

Hogan, T. (2007). Psychological testing: A practical intro-duction (2nd ed.). Virginia: John Wiley & Sons.

Keppel, G., & Wickens, T. D. (2004). Design and analysis.A researcher’s handbook (4th ed.). Upper Saddle River,NJ: Pearson Education International.

Kraemer, K. L., Dedrick, J., & Sharma, P. (2009). One laptopper child: Visions vs. reality. Communications of the ACM,52(6), 66–73.

Leithwood, K., Seashore, K., Anderson, S., & Wahistrom, K.(2004). How leadership influences student learning.Ontario Institute for Studies in Education at University ofToronto. Ontario: CAREI: Center for Applied Researchand Educational Improvement.

Luckin, R., Bligh, B., Manches, A., Ainsworth, S., Crook, C.,& Noss, R. (2012). Decoding learning: The proof, promiseand potential of digital education. London: Retrieved fromNesta.

Mercer, N., & Littleton, K. (2007). Dialogue and develop-ment of children’s thinking: A sociocultural approach.New York: Routledge.

Nugroho, D., & Lonsdale, M. (2010). Evaluation of OLPCprograms globally: A literature review. Australian Councilfor Educational Research.

Nussbaum, M., & Díaz, A. (2013). Classroom Logistics:Integrating Digital and Non-digital Resources. Computers& Education, (69), 493–495.

Prieto, L., Villagrá, S., Jorrín, A., Martínez, I., & Dimitriadis,Y. (2011). Recurrent routines: analyzing and supportingorchestration in technology-enhanced primary classrooms.Computers and Education, 55, 1214–1227.

Roschelle, J., Dimitriadis, Y., & Hoppe, U. (2013). Class-room orchestration: synthesis. Computers & Education,69, 523–526.

Sharples, M. (2013). Shared orchestration within and beyondthe classroom. Computers & Education, 69, 504–506.

Shin, Y., & Raudenbush, S. (2011). The causal effect of classsize on academic achievement: multivariate instrumentalvariable estimators with data missing at random. Journalof Educational and Behavioral Statistics, 36(2), 154–185.

Appendix I Table of Contents for the CurricularWork Covered During the Study

Math topic

1. Place value in numbers up to 10 0002. Numbers in expanded form3. Division with and without remainders4. Ordering fractions5. The sum of common fractions with different

denominators6. Calculating using rational numbers (fractions)7. Dividing with rational numbers (fractions)8. Converting fractions to decimals9. Reading and writing decimals

10. Locating decimals on number lines11. Multiplying whole numbers and decimals12. Multiplying and dividing whole numbers and decimals13. Perimeter of shapes14. Measuring and estimating the size of an angle15. Interior angles and regular polygons16. Rules for the sides of a triangle17. Intersecting and parallel planes18. Interpreting information from simple bar charts19. Interpreting information in a table

A. Díaz et al.216


http://en.wikipedia.org/wiki/OLPC_XO-1

Appendix II Orchestration Example (Class 1 of 3)

Class 1: Content presentation (2 activities, 45 min in total)

Obs. Time Teacher guidance Resources

15 min ACTIVITY 1Objective: To recall prior knowledge (fractions and decimals) inconjunction with students in order to understand new content(converting and forming relationships between fractions and decimals).Expected results: Students are expected to recall concepts previouslystudied in order to undertake the following introduction, practice andapplication activities on converting and forming relationships betweenfractions and decimals.Begin the class by briefly outlining the challenges of introducing decimaland fraction content.In order to help students recall the characteristics of fractions, ask themthe following questions:• ‘Can anyone give an example of a fraction?’ Ask four children to

provide examples.• ‘What are its different parts?’ Together, go through the numerator

and denominator.• ‘Can anyone give an example of a decimal? What are its different

parts?’ Together, go through the whole number and decimal number.Ask your students to ‘note down a decimal in your exercise book’,distinguishing between the parts of a decimal. Pay close attention tothis task, and then ask four pupils to come to the front of the class andwrite their examples on the whiteboard. This way, the exercise can bechecked and discussed by the class, noting the parts of a decimal.

• Next, tell your students, ‘Now, note down a fraction, once morenoting down its different parts’.

Having watched the students complete this task, asking four of them tocome to the front of the class to write their examples on thewhiteboard is recommended. This ensures a clear identification of therequired work.

• To complete the introduction to the class, ask ‘Where can we finddecimals in day to day life? What could be represented by a fractionwithin the classroom?’ Discuss different examples with yourstudents, ensuring the necessary prior learning for the study of thiscontent.

• Each student’s exercisebook

• Whiteboard• Pencil• Eraser

30 min ACTIVITY 2Objective: To understand how to convert fractions to decimals and viceversa.Expected results: Students are expected to learn how to convertfractions to decimals, and vice versa, through real situations in whichthe content applies to day-to-day contexts.Tell your students that they will now learn more about how to convertfractions to decimals and vice versa. Begin by showing them thePowerPoint (PPT) presentation ‘Converting fractions to decimals’. Thisresource explains the relationship between decimals and fractions usingpictorial representation. Two friends, Gustavo and Ignacio, present thenew material and finish with different exercises to help with feedbackand the clarification of possible doubts.To conclude this activity, it is recommended that students complete the‘Using your calculator’ game-based activity in order to consolidate theirlearning.To end the class, ask your students to answer the following questions:‘How do you convert a fraction into a decimal? And how do you converta decimal into a fraction?’ Ask for examples and remember to answerquestions, thereby helping students to understand the content.

• Teacher’s computerfor the PPT (01.Presentation –converting fractionsinto decimals)

• Projector• Game-based activity

‘Using your calculator’(02a. Game-basedactivity – using yourcalculator, conversions)



Appendix III Observation Schedule

1. Class details

School DateTeacher GroupClass number ObserverClass type Presentation Practice Application

2. Activity record

Activity descriptionPeriodObjectiveResourceDuration (min)Support received




Options for filling in activity record

Period Objective Resource Support received

1 Introduction 1 Introduction 1 XO 1 No support2 Practice 2 Presentation 2 PPT or PDF 2 Teaching advisor3 Wrap-up 3 Practice 3 Study guide4 Non-instructional 4 Test 4 Exercise book/Textbook

5 Other 5 Other (record different softwareas distinct resources)

3. Record of observation

Class structure (prepares the class, uses suggested resources, adequately defines periods throughout the class)Record of observations:Use of planning (uses time organization strategies, suggested methodology and questions; prepares suggested teaching

resources)Record of observations:

Integration of planning and resources during classRecord of observations:Content explanationRecord of observations:Feedback on content work (responds to questions, provides feedback, explains possible mistakes, positively reinforces the

content learned)Record of observations:Group management strategy during activity (Observes student behaviour, keeps attention focused on the activity, monitors

student behaviour)Record of Observations:

A. Díaz et al.218


Appendix IV Observation Criteria

Class structure (prepares the class, uses suggested resources and properly defines periods throughout class)

Optimum The teacher explains the class objectives, develops activities consistent with the teaching objective andincludes both an appropriate introduction and wrap-up activity

Good The teacher explains the class objectives, develops activities consistent with the teaching objective andincludes an appropriate introduction or wrap-up activity

Regular The teacher explains the class objectives, develops activities consistent with the teaching objective, butwithout including an appropriate introduction or wrap-up activity

Insufficient The teacher does not distinguish between the different periods of class (introduction, practice, wrap-up)

Use of planning (uses time organization strategies, suggested methodology and questions; prepares suggested teachingresources)

Optimum The teacher uses all planning resources, time organization strategies, methodology suggestions and theuse of questions to give continuity to the different class periods

Good The teacher uses all planning resources, methodological suggestions and the use of questions, but usestheir own way of dividing class time rather than one set out in the planning

Regular The teacher uses some of the planning suggestions during the development of the classInsufficient The teacher does not use planning at all during class

Integration of planning and resources during class

Optimum The teacher uses planning coherently with the established teaching objective and integrates allsuggested learning resources

Good The teacher uses the planning guidelines and integrates only some of the suggested teaching resourcesRegular The teacher does not use the planning, but does use some of the suggested teaching resourcesInsufficient The teacher does not use the planning or any suggested resource

Content explanation

Optimum The teacher clearly explains the characteristics and complexity of the content, answers questions aboutthe content and raises questions suggested in the guidelines

Good The teacher explains the content to the students and links activities with the class objectivesRegular The teacher answers questions about the content (only when asked by the students)Insufficient The teacher provides no explanation or clarification regarding the content.=

Feedback on content work (responds to questions, provides feedback, explains possible mistakes, positively reinforcesthe content learned)

Optimum The teacher is active in providing individual feedback to students based on direct observation andstudent demand

Good The teacher provides feedback on work, based on direct observation and student demandRegular The teacher provides feedback on content work, attending to students only when they request

assistanceInsufficient The teacher provides no feedback on student work, scarcely supervising at all

Group management strategy during activity (observes student behaviour, keeps focused on the activity, monitorsstudent behaviour)

Optimum The teacher maintains control and attention of the entire class, monitors student work, answersquestions, supports students, is proactive and approachable, and encourages student participation

Good The teacher maintains control of the group during activities, answers questions and helps those studentsrequiring support

Regular The teacher maintains control of the group, attending to students only when they request assistanceInsufficient The teacher barely maintains control of the group, does not support the weakest students, has scarce or

zero interaction with the class and is not dynamic



Orchestrating the XO computer with digital and conventional resources to teach

Documents

Transcript of Orchestrating the XO computer with digital and conventional resources to teach