Exploring young students' talk in iPad-supported collaborative learning environments

Highlights

• Using iPads as public learning devices can enhance young students' exploratory talk.

• Using open-design apps with young students can stimulate exploratory talk.

• iPad portability, viewing angle, and interface support student collaboration.

• iPads and open-design apps provide a useful medium to improve student talk quality.

Abstract

In the few years since its release, Apple's iPad has generated much discussion about its potential to support student learning at all levels of the education system. Much of this has focused on its physical and technical attributes, such as portability, touch-display, connectivity, and large array of apps. However, a few studies have begun to explore possible advantages of iPads being used as public work spaces, enabling students to interact more collaboratively when creating learning outputs. These studies point to other affordances such as the iPad's ability to lay flat on a desk or be propped at a convenient angle, its wide viewing range and multi-user accessible interface, as being particularly relevant in supporting collaboration.

Between June and November 2013, researchers from the University of Waikato used a specifically developed ‘observeware’ app to capture display and audio data while young students (5 year olds) were using iPads in pairs for developing numeracy, literacy and problem-solving/decision-making skills. The study used Mercer's (1994) talk types framework to explore the nature of talk students engaged in while they were using the iPads and interacting with each other and their teacher, and also how features of the device may have influenced this.

Results indicated exceptionally high levels of on-task talk, but that this was mostly of an affirming and non-critical nature and unsupportive of outcome improvement or refinement. While the iPad offered unique potential as a shared, public learning device, the pedagogical role of the teacher in realising this by helping students learn appropriate ‘ground rules’ to raise talk quality, was critical. This article details the methodology used and the results of the study. It discusses the important role teachers play in helping young students build oral-interaction strategies to capitalise on high levels of learning engagement, and the unique features of these devices.

Introduction

Since their launch in early 2010, the iPad has stimulated much interest at all levels of education as a breakthrough or “game changer” (Geist, 2011, p. 758) learning device. Unique features such as its touch screen interface, light and compact form factor, ubiquitous wireless connectivity and wide array of apps, have been cited as offering unique affordances particularly suited to educational use (Dhir, Gahwaji, & Nyman, 2013). While much early commentary took the form of promotional hype or teacher and newspaper stories, recent studies have emerged of a more substantial nature, illustrating outcomes from iPad use in different learning contexts ranging from special education (e.g., Miller, Krockover, & Doughty, 2013) through to tertiary settings (e.g., Cochrane et al., 2013, Geist, 2011). Other studies have explored their use for particular purposes such as promoting early years literacy (e.g., Falloon, 2013a, Getting and Swainey, 2012, Hutchison et al., 2012, McClanahan et al., 2012), written language (e.g., Falloon, 2013b) and STEM concepts (e.g., Aronin & Floyd, 2013).

Recently, attention has focused on an observed ‘engagement factor’ when students use the devices, and how they appear better capable than other technologies such as laptops and desktop computers, to promote learner collaboration. An interesting study undertaken by Fisher, Lucas, and Galstyan (2013) compared using iPads and laptops with student pairs for teaching business calculus. Their observational study of students using both devices revealed significant benefits from using iPads, if learner collaboration is a goal. They determined one of the main advantages was the iPad's ability to support “transition back and forth from private to public work spaces” (p. 165). That is, their design (portability, large screen, multiple viewing angles, ability to be manipulated by more than one person etc.) enabled the device to act both as a private work space and as a “public centre of communication” (Fisher et al., 2013, p. 176). They concluded this supported collaboration throughout a learning task. Laptops, on the other hand, tended to be used more privately, the screen and keyboard in particular acting as barriers to collaboration, leading to the “sharing of information only at the conclusion of a problem” (p. 176). However, they acknowledged limitations to their study in terms of its reliance on observed actions. They suggested that additional research was needed that explored the nature of student dialogue associated with collaborative action, to better determine how device interaction impacts upon the way students discuss mathematics.

Some studies have pointed to perceptions of enhanced learner on-task engagement when using iPads (e.g., Henderson and Yeow, 2012, Manuguerra and Petocz, 2011). Others have offered a contesting view, claiming that the device distracted students from intended learning due to challenges involving unrelated apps and websites (Rossing, Miller, Cecil, & Stamper, 2012), or pop up advertisements (Falloon, 2013a). A recent study by Hoffman (2013) undertaken in a 1:1 iPad classroom explored students' engagement with learning tasks using iPads, and specifically, whether or not their perceptions of levels of engagement (defined as on/off task behaviour) matched observational data. Data for her study of 55 English class students aged 14 and 15 were collected using classroom observation (on/off-task tallies and field notes) and whole class discussion (prompted dialogue on the 1:1 programme and any affect on learning behaviour). Her findings were mixed, and suggested that while students observationally demonstrated high levels of on-task response, this was due more to the perceived importance of the task, the extent to which the task was engaging, and the teaching style of the teacher. Students rated highly personalising the device and the ability to set it up according to individual preferences. They linked this with effectiveness and efficiency by reducing the need to adjust settings or adapt to multiple organisational systems, as was often the case when devices are shared. Countering these, negative comments were made that it was easy to disguise non-learning activity such as messaging or social networking, due to the ease with which apps could be shuffled. Other comments highlighted student difficulties in learning using a visual display – that is, they perceived they learnt better when they needed to “physically write the words out, instead of just pressing buttons” (Hoffman, 2013, p. 15).

Apart from these studies, very little research has been undertaken exploring how device affordances such as those mentioned by Fisher et al. (2013) and Hoffman (2013) may affect the way young students learn when using iPads in pairs or small groups. However, considerable empirical evidence exists demonstrating how learning with and through technology can help develop skills such as student collaboration, interactivity, communication and negotiation, when engaged in socioculturally-based learning tasks (e.g., Goodfellow, 2001, Hollan and Stornetta, 1992, Roschelle et al., 2010, Staarman, 2009, Zurita and Nussbaum, 2004).

Neil Mercer's early research exploring student group talk while engaged in computer-based learning provided some insights into the nature of their collaboration, and how language they used assisted them (or not) to construct knowledge needed to solve learning problems. In the SLANT project (Spoken Language and New Technology), Mercer (1994) explored “the quality of talk in computer-assisted collaborative activity” (p. 24) to evaluate its nature, and “better understand the role of the teacher in supporting computer-based talk activities” (p. 25). He was also interested in learning more about software design, and its influence on children's talk.

Groups of primary school students were videoed working on a range of curriculum-related computer learning tasks, and an analysis of their conversations was carried out to identify the nature of talk they engaged in. Mercer identified three distinct ‘talk types’ that he classified as disputational, cumulative and exploratory. Disputational talk was ‘argumentative’ in nature, where students offered challenge to each other's ideas, but without justification or offering alternatives. Cumulative talk was more conciliatory, and typically represented agreement or continuance without the argumentative elements of disputational talk. Exploratory talk supported reasoning, and displayed student capacity to interact with “the reasoned arguments of others when drawing conclusions, making decisions, and so on” (Mercer, 1994, p. 27). Mercer cautioned against judging one talk-type as being inherently better than the other, as each had its place in the appropriate context. However, he speculated that computer-supported activities designed to promote exploratory talk were the most desirable, given broader educational goals of developing critical thinking and reasoning capabilities.

Mercer also identified four variables that strongly influenced the quality and nature of student talk. These were the physical attributes and design of the hardware, layout and organisation of the equipment, design and content of software, and the nature of the learning task. He commented that it was difficult to extract individual levels of influence of each of these variables on student interaction, as each in some way affected the others and stimulated different types of talk. However, an interesting finding relevant to this study was the powerful role of software design in promoting talk of an exploratory nature. Mercer determined that software of an open design – that is, requiring students to generate their own content or negotiate solutions to open-ended puzzles or challenges, prompted the most exploratory discussion; whereas software of a closed, highly-structured design (such as games and drills) generated “very little extended, continuous discussion of any kind” (p. 29).

Building on his earlier research (Edwards and Mercer, 1987, Mercer and Edwards, 1981), Mercer (1996) strongly argued that teachers should assist students to develop understanding of ‘ground rules’ that encourage talk supportive of solving intellectual problems, and the joint construction of knowledge. He described these as “explicit norms and expectations that it is necessary to take into account to participate successfully in educational discourse” (p. 363). Far from being a common sense consideration, Mercer claims understanding how group computer-supported learning (CSL) tasks are carried out, and the oral skills best suited to achieving successful outcomes, need to be made clear, and if necessary, taught, modelled and practised with students. His research revealed that often students involved in CSL appeared to be “operating disparate sets of ground rules for talking and collaborating” (p. 371). He found little evidence of talk suggesting thoughtful evaluation of information, constructively critical appraisal of others' contributions, or shared decision-making. However, following structured teaching interventions designed to promote exploratory talk, considerable increases were noted, in addition to higher levels of task enthusiasm and involvement. These interventions included learning tasks requiring information and idea sharing, the offering and supporting of assertions and opinions, questioning, negotiating agreement, and collective responsibility for outcomes. Mercer's early work provides insights into the potential of group CSL to construct powerful environments for fostering and extending talk as a “social mode of thinking” (p. 374). That is, it highlights the opportunity CSL environments present for improving the performance of student talk as an aid to joint knowledge construction. However, it alerts that this cannot be taken for granted, and that the teacher has a pivotal role to play to ensure this potential is realised.

Mercer's framework has been used in a recent study by Kucirkova, Messer, Sheehy, and Panadero (2014) that explored the engagement and talk of pre-school children with a story creation app (‘Our Story’) and a small range of colouring, drawing and construction/puzzle apps. They used a combination of Bangert-Drowns and Pyke's (2001) taxonomy of student engagement with educational software and Mercer's exploratory talk, to interpret video and audio data from forty-one 4 and 5 year old Spanish pre-schoolers, who were using the apps unsupervised during free-choice activity time. Interestingly, their analysis indicated qualitatively different levels of engagement with each type of app. The story creation app appeared to be more effective for engaging the students critically, and in a way that sustained deeper and more challenging interactions. While little difference was noted in the total percentages of exploratory talk for each app type, Kucirkova et al., did note that there was less evidence of extending and challenging exploratory talk in students' use of the drawing or colouring apps, and less overall exploratory talk when students used the puzzle/construction apps. Commenting on possible reasons for this, they speculated that the open-ended nature of the story and colouring apps, and the fact that they had no built-in ‘success criteria’ (e.g., affective reinforcements) stimulated more discussion, as the children needed to verbally interact to gain feedback and confirmation from peers. Consistent with Mercer's (1994) earlier findings, Kucirkova et al. concluded that using open-ended apps could provide valuable opportunities for children to develop exploratory talk. They also, however, acknowledged limitations to their study in terms of the amount of data collected, the study's duration, and restriction to pre-school environments where use of the apps is generally “uncontrolled and spontaneous” (p. 182).

What follows builds on Mercer's work and that of Fisher et al. (2013) and Kucirkova et al. (2014). It explores young students' talk when using iPads collaboratively in pairs to plan writing tasks and create content for units of learning. It applies a variation of Mercer's ‘talk type’ framework to examine the nature of their talk, and discusses opportunities for teachers to use the iPad's public work space affordance (Fisher et al., 2013) to provide opportunities for students to interact with the device and each other, in a way supportive of developing exploratory talk. It uses a unique display recorder app installed on the iPads to capture video and audio data independent of observer or ‘over-the-shoulder’ video effects.

The collection and analysis of data was informed by the following research questions:

• What is the nature of student talk when planning and creating literacy-based content in pairs using iPad apps?

• How might teachers exploit the iPads public work space affordance to foster talk of a more exploratory nature?

Participating students were a class of year 1 (5 year olds) attending a medium-sized primary (elementary) school located in a small town in the Waikato region of New Zealand. The school had a roll of 350 students, with the research class comprising 19 students (10 girls and 9 boys). All students had been at school for between three and six months, and had been using iPads as part of their literacy and numeracy programme. Ten iPad 3s were supplied by the University for the research, having been made available from April 2013 to allow time for the students to become familiar with their operation. The teacher was an experienced practitioner having taught for 17 years in classes from years 1–8, with the last five of these being at year 1.

Data were collected using a display capture app on seven occasions from July to November 2013. Each session was between 40 min and an hour in duration. The three apps used by the students during data collection were selected by the teacher to be compatible with the learning objectives of her broader literacy programme. She selected apps that the students needed to actively engage with by creating their own content, rather than apps of a ‘consumption’ design where they merely responded to onscreen prompts or cues, such as in many learning games. In finalising her choices, she accessed online reviews from other users, read commentary from teachers on the New Zealand Virtual Learning Network (see www.vln.school.nz/), and trialled them with her own primary aged children. She selected the following apps to help meet the learning purposes specified (Table 1).

Data were collected using a unique display capture tool adapted from developer code associated with a Cydia App called Display Recorder. The ‘observeware’ app records in the background while students are using their apps, creating a video (with audio) of all display activity. This can then be downloaded onto a laptop for later analysis, using a root file retrieval application. No signs of recording apart from a finger placement indicator (a white dot on the display) are visible to the students. A typical recording screenshot including finger placement indication can be seen in Fig. 1. Further details of the tool and methodology have been reported elsewhere (see Falloon, 2013a, Falloon, 2013b).

The 19 students worked in teacher-assigned pairs and one group of three (forming eight pairs and one threesome). Across all groups a total of nearly nine hours of video and audio were recorded. This comprised two or three separate sessions for each pair, usually of between 20 and 30 min each in duration. Data sets for each pair could easily be collated, as the composition of the pairs remained the same throughout the study. From each data set, one episode was selected for talk-type analysis. They were purposively selected after an initial appraisal of all data, to illustrate the best-recorded evidence of different talk types ‘in action’. The results for all pairs are summarised graphically in Fig. 2.

It should be noted at this point that Mercer's original framework was developed using data from slightly older students (8–11 years). While his broad classifications of disputational, cumulative and exploratory talk are used here, a slightly different coding regime to that used by other iPad studies adopting his framework (e.g., Kucirkova et al., 2014) has been applied. This is to accommodate any differences in the nature of evidence younger children may provide – specifically, the complexity and sophistication of language used, and taking into account the way in which intent or meaning is communicated (e.g., tone of voice, expression etc.). For this reason, coding decisions were made using sub codes defined by detailed descriptors generated from data, which were then aligned more specifically with Mercer's classifications (Table 2). Instead of using only keywords, evidence was expanded to include phrases and whole sentences that were judged to reflect interactions consistent with the descriptors, and aligned with the sub codes. Examples of these are included in the sample data tables (Table 4, Table 5, Table 6).

During initial coding, all data sets (episodes) were reviewed several times to fully understand the nature of talk occurring, and its relationship to Mercer's classifications. From this, sub codes and descriptors were generated (Table 2) that formed the coding template that was applied to the selected episodes. Studiocode video analysis software² was used to code these. Using Studiocode meant that detailed transcription was not needed, as selected clips aligned with the sub codes are automatically arranged on timelines and can be individually or collectively reviewed at will.³ For the purposes of this article, selected verbatim excerpts from these recordings have been included in the data tables.

The sub codes were entered into a Studiocode template that was applied to the selected episodes. Two non-active labels were added to the template (knowledge/decision-making and working relationship) as well as three active talk type classifications (teacher–student, working and other activity). The former were added as additional non-active descriptive labels that variously linked to the different talk types, while the latter were active code buttons allowing coding of recorded talk other than that aligned with Mercer's original classifications (and their sub codes). Specific details and descriptions of the sub codes expanded from Mercer's talk-types framework is provided in Table 2, and samples of data coded under each are included in Table 4, Table 5, Table 6.

An example of the coding template as applied to Pair 1's episode can be seen in Fig. 3. The sub codes and the added classifications were active buttons, and registered on the timeline an event in the video aligned with the particular code or classification. Studiocode also produced statistical summaries of data giving the total times aligned to each code, the number of occurrences, and the mean occurrence time. Summaries for the three illustrative episodes are included in Appendix A.

To support coding reliability, an excerpt from a single data sample for three pairs (1, 2, & 8) was blind reviewed by a post-graduate research assistant. Inter-rater agreement calculations (κ) for each of the three main talk type classifications are provided in Table 3. Following Gwet's (2012) advice, calculations were made only on instances both coders had identified, to lessen the likelihood of an underestimation of agreement probability. While agreement calculations were not completed for the sub codes, occurrences from the excerpts were debated, with eight changes to coding decisions subsequently being agreed upon. According to Landis and Koch's commonly-used scale, agreement strengths ranged from moderate to substantial across the three main talk types (Landis & Koch, 1977).