Garry Falloon Associate Professor Faculty of Education University of Waikato Hamilton, New Zealand
Abstract
Since its introduction in 2010, Apple’s iPad has received much attention from education commentators, citing its unique touch screen, portability, relative low cost and huge array of apps, as offering significant potential to support learning at all levels.
This paper summarises key findings from the first two phases of a 3-year study exploring primary school students’ use of iPads and apps in general class settings. These phases focused on using iPads for developing foundation literacy, numeracy and problem-solving skills, and analysed the nature of oral discourse that occurred while students were completing iPad-based learning tasks.
Data were collected using a specially-developed ‘observeware’ app that recorded the iPad’s display and student verbal interaction while they were working with a range of open and closed-design apps.
Findings highlight a complex relationship existing between student knowledge and dispositional factors, peer-interaction, and app design, content and features that influences the quality of learning students generate. Furthermore, they suggest using open-design apps in pairs or small groups can provide valuable opportunities to develop exploratory talk, when iPads are used as public work space devices.
This paper will present illustrative data from the study, and raise considerations for teachers, researchers and app developers to help inform more effective designs and use of apps for learning.
Introduction
While much rhetoric surrounds the advent of iPads to the array of digital resources available to teachers and students (Apple Inc., 2014), limited empirical research presently exists analysing how their much-heralded features, such as touch screen interface, huge range of low-cost ‘educational’
apps, portability and connectivity, offer unique possibilities for supporting student learning. While some qualitative, perceptions-based studies have been undertaken, many of these have focused on information management or logistical efficiency benefits, such as supporting moves towards paperless environments or advantages from ‘anywhere, anytime’ access to information and online services (Shepherd & Reeves, 2012). Other studies have documented often anecdotal perceptions of learning and motivational advantages from iPad use in special education (Cumming & Strnadova, 2012; Jowett, Moore & Anderson, 2012), early years literacy development (Dobler, 2012; Getting &
Swainey, 2012; Harmon, 2012), pre service teacher education (Saine, 2012) and English language learning (Godwin-Jones, 2011).
Media reports have also highlighted moves by a number of schools towards ‘Bring Your Own Device’
or BYOD programmes, where parents are encouraged to purchase tablet devices such as iPads for their children to use at school, in much the same way as conventional stationery (Bilby, 2013; Irwin
& Jones, 2014). Some schools have even gone as far as providing a device for each student (Jones, 2014; Moran, 2014). However, until recently few studies have moved beyond the use of qualitative,
self-report data in attempting to reveal more about the potential of these devices to support student learning. In particular, limited visible evidence has been gathered of the specific nature of students’
interactions with each other and the device while using them to solve learning problems, and the influences on this process.
From early 2012, a researcher from the University of Waikato in Hamilton, New Zealand, has used a unique display recording app to gather video and audio data accurately portraying young students’
interaction pathways and strategies when using iPads and selected apps for a range of learning tasks.
This paper describes the methodology and summarises the main results from the series of studies. As detailed outcomes from each study have been published separately elsewhere (Falloon, 2013a;
Falloon, 2013b; Falloon & Khoo, 2014), it provides a synthesis of these and draws out implications for practice.
Research Establishment and Context
Since the launch of the iPad in 2010, some commentators have pointed to the relative affordability of iPads as the key to addressing the perennial issue of access to sufficient devices to make them useful in a conventional classroom (eg., Conn, 2012). However, the reality is that few schools have sufficient funds to purchase devices for their students, instead relying on BYOD-type programmes to address this issue. Acknowledging this fact, in late 2011 an application was made to Waikato University’s Education Faculty research fund to purchase eight iPads. These were to be used in the junior area of the primary school on a one-device-per-student-pair basis, to investigate their potential to support literacy and problem-solving skill development. The selected school was a decile2 5 contributing primary (Years 1-6) with a roll of 360 students, located in a small semi-rural town approximately 20kms from Hamilton city.
The school was chosen following a positive response to a personal communication inviting participation, and followed on from previous successful studies the researcher had undertaken with the school. Junior students were targeted following indications from other research that suggested evidence of enhanced learner engagement from iPad use contributed to significant literacy learning gains in young children (McClanahan, Williams, Kennedy, & Tate, 2012). Subsequently, eight iPad 3s were purchased in February 2012, and research foci and goals collaboratively negotiated with the school. A broad implementation plan was also developed, providing a structure to support the study for its first year. This was subsequently revised and redeveloped for the second year, to reflect emergent findings from year 1.
After discussion with the principal and because a number of junior school teachers indicated keenness to be involved, ‘expressions of interest’ were called for from teachers wishing to participate. This required them to respond to specific criteria relating to their motivation for involvement and outlining their pedagogical and curriculum strengths, as well as suggesting ways in which the devices could be used to support the research foci within the context of their classroom programme. The school’s senior management team selected an experienced practitioner, based on her “history of receptiveness to innovation, and very sound, child-focused pedagogy” (Principal, interview, June 2012). The selected teacher, Tonia, had been teaching for 16 years, the last 5 of which had been at the school in year 1-3 classes. At the time data collection commenced she was in her third year of teaching new entrant/year 1 classes. A series of planning meetings were held with Tonia during May and June 2012, during which specifics of data collection for Phase 1 were negotiated. It was decided that data aligned with each research question would be collected separately across the two years, due to their dependence on students using apps of different designs for different learning purposes.
2A full explanation of the New Zealand school decile system can be found at
www.minedu.govt.nz/Parents/AllAges/EducationInNZ/SchoolsInNewZealand/SchoolDecileRatings.as px
Research Classes and Questions
The research was structured with two different classes – the first phase focused on question 1 (data collection from July-December, 2012) and the second phase on question 2 (data collection from June – November, 2013). The 2012 class comprised 18 students (11 girls and seven boys, making up nine pairs) while the 2013 class numbered 19 students (10 girls and nine boys, making up eight pairs and one threesome).
The following questions guided data collection for the study:
1. How do design and content features of selected apps used on iPads affect the learning pathways of pairs of young students using them independently for problem-solving tasks?
2. What is the nature of student talk when planning and creating literacy-based content in pairs using open-design iPad apps?
Research Method and Data Collection
Across both years, case study method located within an interpretive theoretical framework informed the research approach used. Following unsuccessful trials of over-the-shoulder video and observation for data collection that resulted in ‘staged’ student performances, university computer support personnel adapted code from a Cydia app called Display Recorder that allowed recordings to be made of the iPad’s screen and audio via the built-in microphone, while students were working. The app also recorded using a white dot student finger placement on the display, so actions associated with the video and dialogue were captured (see Figure 1). The recorder was activated via a combination of finger taps on the left top corner of the display, but no other evidence of the recorder’s operation was visible to students. Following each recording session (of between 25-40 minutes) the video files were downloaded to the researcher’s laptop for later analysis. In all, nearly 72 hours of student data were collected across the 2 years, of which just over 37 were analysed using Studiocode.
Figure 1. Screenshot from Pic Collage showing recorder finger-placement indication
App Selection and Device Organisation and Use
In both research phases, iPad use was integrated with the normal classroom programme as much as possible. The apps were selected by Tonia to meet what she judged to be the learning needs of her students, within the curriculum topics being studied over the course of data collection. Selections were made following appraisal of reviews in Apple’s App store, online and own-school assessments made by other teachers, and a personal evaluation based on use with her own primary-aged children.
Apps selected for Phase 1 were of a problem-based, ‘learning game’ design (see Appendix A). They required students to work together to complete literacy (mainly spelling and phonics) and numeracy (number) learning problems, often embedded in game-like formats. They were generally of a closed design – that is, students were required to work within defined parameters imposed by the structure and format of the app, usually responding to cues and prompts to select from a range of provided responses, or enter their own in pre-set fields.
Phase 1 apps were organised into separate folders according to the different days of the week, and were changed regularly. This decision was made following early realisation that having access to many apps at the same time led to a ‘lolly scramble’ effect, where students skimmed from one to another without substantially engaging with any. During Phase 1, use of the iPad was integrated into the class’s literacy tumble so different pairs of students from targeted reading groups could access the devices at different times, as shown in Figure 2. Within each reading group pairs were teacher- selected, and remained stable for the duration of the trial.
Figure 2. The Literacy Tumble Planner incorporating the iPads
The apps selected for Phase 2 were of a more open design, allowing students to generate and input their own content far more flexibly, in response to learning outcomes rather than app-imposed parameters. Apps used in this phase comprised mindmapping (Popplet), graphic/design (Pic Collage) and oral language/storytelling (PuppetPals HD). In Phase 2, the whole class accessed and used the apps for units involving story planning (Popplet), celebrations (Pic Collage) and drama recount (PuppetPals HD).
Data Coding
Studiocode is an analysis tool for coding video data according to identified themes or repeated occurrences existing across datasets. It allows the creation of analysis tags or labels identifying specific incidents within videos aligned to a particular theme, which have usually been identified through an initial review of a data sample. Coded incidents can be replayed collectively by activating the appropriate code label on a timeline, or single samples accessed individually by double-clicking (see Figure 3). Due to the time-consuming nature of coding video data and resource constraints, not all data were coded. Data aligned with each question were purposively selected for coding after an initial appraisal was made to ensure a balanced coverage of apps used, inclusion of at least one sample from each student pair, and representation within all curriculum topics where apps were used. Overall, 24 hours of video were coded from Phase 1, while just over 13 were coded from Phase 2. To enhance reliability, the researcher employed a postgraduate student to carry out a blind review of data samples.
Inter-rater agreement calculations were then performed on the samples using Kappa coefficient (K).
After some adjustment and negotiation, coding agreements in Landis and Koch’s (1977) good to substantial range were secured3.
3Further details of this process can be found in Falloon, 2013a; Falloon, 2013b; Falloon & Khoo, 2014.
Figure 3. A Studiocode coding window showing timeline, code labels and video sample for Phase 2 data
Results Summary
Detailed tables, results and analysis of data from both research phases have been published elsewhere, and due to space constraints, will not be repeated here (see Falloon, 2013a; Falloon, 2013b; Falloon
& Khoo, 2014). What follows is a summary of the main findings from each phase, and a discussion of the implications they hold for teachers integrating iPads into their classroom programmes.
Phase 1
This phase focused on analysing student learning pathways while using selected ‘closed-design’ apps.
In particular, it targeted the relationship between app design and/or content features and student interactional strategies, in an attempt to discover if and how particular combinations of these supported (or not) their learning progress. Figure 4 summarises the findings from this phase. It depicts the interaction of four key ‘drivers’ – knowledge, cognitive effort and strategy, device/app content design and response, and student work techniques, as influential in determining the quality of
‘learning value’ students derived from their use of the apps.
Data clearly indicated the cornerstone of quality learning interactions with the apps was the existing knowledge students, metaphorically-speaking, ‘brought to the table’. This knowledge took two forms – declarative (knowing what content and conceptual knowledge was needed to solve the problems) and procedural (knowing how to solve the problems - both technically and conceptually). While the apps created highly engaging and motivating interactional environments, due to design limitations restricting the nature and type of feedback they gave in response to student inputs, their capacity to help students generate new knowledge or remedy mistakes, was limited.
Figure 4. Factors influencing students’ learning pathways while using the apps (from Falloon, 2013a)
A characteristic of most of the learning game apps used to provide only positive or negative affective feedback (eg., hand claps, cheers, animations, star/reward charts or ‘try again’ type voiceovers) did not assist students to learn new knowledge or the strategies needed to advance their learning. Virtually no apps provided feedback of a formative or corrective nature, which would have allowed students to analyse mistakes and learn from them, to improve future performances.
Additionally, a noticeable trend during Phase 1 was the diminished benefit of affective feedback from the start of data collection compared to the end. Put simply, the more feedback of this nature students received, the less impact it appeared to have. In fact, towards the end of Phase 1 data collection, affective feedback appeared to inhibit the quality of some students’ learning. End point data contained several examples of students deliberately inputting incorrect responses or randomly guessing answers in order to beat their workmate to the finish, which more often than not was marked by an entertaining image or animation. A good example of this was seen in the app Rocket Speller, where students were often recorded challenging each other to see how many pieces they could get the rocket to explode into upon its return to earth, by making it fall the quickest (see Figure 5).
This phenomenon was labelled gamification, and was a very common occurrence in data. Basically, it referred to students diverting their attention from learning engagement with the app to entertainment engagement. Gamification was generally triggered by two scenarios. The first, which was labelled ‘app fatigue’, came from student over-exposure to, or over-familiarity with, an app or apps. If an app was used too frequently, these young students quickly became bored or mastered techniques that enabled them to skip through parts they found repetitive, unappealing or routine, to get to entertaining or game content. While restricting the numbers of apps in the daily folders helped lessen the ‘skimming’ effect, it was equally important to ensure there was regular turnover to avoid app fatigue.
Figure 5. The exploding rocket in the app Rocket Speller
Gamification also occurred in situations where app content became too difficult for students to work on independently. Most often this resulted from apps automatically increasing the level of content difficulty
in response to students’ answers, or where initial level selection had been set beyond the students’
capability (or they had done this themselves). While a few students displayed a combination of perseverance, effort and strategy that allowed them to attempt more difficult problems, all eventually reached a point where their cognitive and affective resources ‘ran out’. At this stage some reverted to gamification, while others opted to close the app and select a different one. This latter characteristic was labelled bailing out (Figure 4). As previously, the absence of built-in scaffolds or formative feedback was instrumental in limiting the progress students could independently make.
Recordings also provided evidence of different ways these students worked with each other and the iPads, while completing learning tasks. These were coded as collaborative, semi-collaborative and non- collaborative, and were influential in how much progress pairs were able to make. The output from pairs coded as collaborative in Phase 1 (n=4) could best be described as joint efforts, where recorded discourse indicated decisions were negotiated and agreed upon, and where evidence existed of both students having reasonably equal device access in pursuit of a commonly-viewed goal/s. Student pairs coded as semi-collaborative (n=3) at times displayed some of the characteristics of collaborative pairs, but were more inclined towards shared device access (often determined by equal ‘hands on’ time) and separate decision-making, albeit while working on the same app and towards the same goal. Two pairs were coded as non-collaborative, and typically these pairs shared the iPad, but seldom on an equal or equitable basis. Access was usually determined through a ‘battle of wills’ or physical interaction of some type, and generally resulted in one student dominating to the detriment of the other. Although device access time was shared in some way in these arrangements, often students chose to work on different apps, closing their partner’s when their own turn eventuated. Thus for these pairs, progress towards task goals was, at best, incremental.
Data collected during Phase 1 suggested merit in exploring in greater depth the oral interaction between students, as they appeared highly influential on their decision-making when solving learning problems in the apps. This observation provided the direction for the second phase of the research (2013) that focused on analysing student interactional talk while using the iPads for content-creation tasks.
Phase 2
Data for Phase 2 were gathered while student pairs were using apps of an open-ended, content creation design, within literacy-based units of learning. This was a deliberate decision motivated by Neil Mercer’s (1994) SLANT (Spoken Language and New Technology) research with primary school students, that suggested learning benefits could be gained through small group exposure to software where students are required to negotiate and talk when making content-related decisions. Three apps were selected for this phase (Popplet, PuppetPals HD and Pic Collage). Student pairs were once again teacher-selected, but unlike Phase 1, pairings were social and not achievement-based. The pairs had been formed three months into the school year and approximately two months before data collection commenced, and had remained largely unchanged. Tonia made this decision based on her earlier observations of ‘learning efficiency’ benefits from maintaining stable groups, or as she put it, “they seem to settle down more quickly and just get on with it” (Tonia, personal communication, July, 2013).
To help make sense of the recorded oral interaction between students, codes were developed based on Mercer’s ‘talk type’ classifications of disputational, cumulative and exploratory. Briefly, talk coded as disputational indicated defensiveness, disagreement, competition or person-focused conflict, with individuals possessing contributions rather than collaborating in joint content development. Talk coded as cumulative was affirming but non-critical in nature, often building on previous activity but in a non- expansive, passive manner. Exploratory talk was more critical, but focused on critiquing ideas with the goal of improving content, rather than being of a personal nature.
Talk coded as exploratory frequently indicated negotiation, synthesis and respectful cognitive engagement with others’ views, with the purpose being to improve decision-making and content quality.
This talk type, Mercer suggested, should be encouraged, as it is integral to the role of educational institutions in societies where principles of “accountability, (of) clarity, constructive criticism and