# nQuire: Technological Support for Personal Inquiry Learning

Paul Mulholland, IEEE
Stamatina Anastopoulou
Trevor Collins
Markus Feisst
Mark Gaved
Lucinda Kerawalla
Mark Paxton
Eileen Scanlon
Mike Sharples
Michael Wright

Pages: pp. 157-169

Abstract—This paper describes the development of nQuire, a software application to guide personal inquiry learning. nQuire provides teacher support for authoring, orchestrating, and monitoring inquiries as well as student support for carrying out, configuring, and reviewing inquiries. nQuire allows inquiries to be scripted and configured in various ways, so that personally relevant, rather than off-the-shelf inquiries, can be created and used by teachers and students. nQuire incorporates an approach to specifying learning flow that provides flexible access to current inquiry activities without precluding access to other activities for review and orientation. Dependencies between activities are automatically handled, ensuring decisions made by the student or teacher are propagated through the inquiry. nQuire can be used to support inquiry activities across individual, group, and class levels at different parts of the inquiry and offers a flexible, web-based approach that can incorporate different devices (smart phone, netbook, PC) and does not rely on constant connectivity.

Index Terms—Fieldwork learning, nomadic learning environments, learning management systems, instructor interfaces, collaborative learning tools, authoring tools, mobile and personal devices.

## Introduction

Inquiry learning involves learners designing and carrying out investigations in order to acquire knowledge about the domain under study as well as developing skills in the application of the scientific method [ 1], [ 2]. nQuire was developed to provide support for inquiry learning, but more specifically for personal inquiry (PI) learning in which the interests and concerns of the learner motivate how the inquiry is designed and carried out. This work was conducted in the Personal Inquiry project. PI was a collaborative research project between the University of Nottingham and The Open University supported by the UK research councils. An overall aim of PI was to design, deploy, and evaluate a combination of technology and pedagogy for evidence-based learning of personally relevant topics in a scientific way.

A range of trial scenarios were developed and tested during the project in participation with the teachers and students. From these, defining characteristics of personal inquiry can be identified that were of particular importance to the design of nQuire. First, designing and carrying out inquiries that are of greater personal relevance involves taking as a starting point topics and themes of interest to the learner that have an impact on their lives and then formulating inquiries that resonate with these. One advantage of inquiries that connect to student's personal concerns is that they can lead the learner to revisit and reconsider their ideas long after the science class is over [ 3]. Within personal inquiry, the formulation of the inquiry could be carried out by the teacher, drawing on topics of relevance and interest to the students [ 4] or be more student-led, guided by discussions with the teacher [ 5]. Second, personal inquiries incorporate data collection techniques accessible to the students with which they can interrogate their own environment [ 6] or themselves [ 7]. This might involve taking sensor readings, collecting interviews or taking photographs. Third, personal inquiries encompass familiar contexts such as the home and the local neighborhood as well as the school and field trip locations [ 8]. Fourth, personal inquiries involve working with others on shared interests as well as individually [ 9].

The resulting inquiries are therefore imbued with personal context, in terms of their motivation, how they are conducted and the physical environment in which they are situated. These contrast sharply with many other research initiatives within inquiry learning that tend to focus on simulation environments [ 10] that can facilitate learner access to and manipulation of more idealized models of behavior.

Within the PI project we developed nQuire, a web-based software environment to support personal inquiry. As the inquiries are personal, rather than off-the-shelf experiments, functionality had to be developed to allow teachers to author new technology-mediated inquiries and for students to configure inquiries to meet their own needs. We use the term “script” to refer to a specification of an inquiry that can be implemented in software and used to guide teacher and student inquiry activities. This draws on prior use of the term to describe learning processes such as collaboration scripts [ 11].

As well as having authoring and customization functionality, nQuire needed to provide support for four types of inquiry process. First, learners have difficulties managing the regulatory processes of inquiry [ 12]. Regulatory processes are concerned with planning, monitoring, and evaluating progress within the inquiry. Similarly, Quintana et al. [ 13] in their scaffolding design framework for science inquiry identify process management as an aspect of inquiry in need of process support. Regulatory processes can be expected to be even more challenging within personal inquiries that may take place over longer time frames than class-based simulation experiments and encompass locations, such as the home, that limit direct teacher support to monitor and guide progress.

Second, inquiry learning involves managing transformative processes. These are defined as those that yield knowledge as part of the inquiry [ 12]. Similarly, Quintana et al. [ 13] identify sensemaking, and articulation and reflection as processes that transform or create knowledge and need support during science inquiry. Transformative processes can be expected to be particularly challenging in personal inquiry contexts. For example, data are collected in environments such as the home or local area in which the data can be noisier and more variable than in a controlled laboratory setting. The process of data collection can also be more error prone, particularly when conducted without teacher assistance.

Third, collaboration and sharing is an important feature of personal inquiry, as the nature of the inquiry is not predefined, but rather emerges from dialogue and negotiation with the teacher and fellow students. As the inquiries connect to personal concerns and experiences [ 3], they can also be expected to generate more varied opinions, assumptions, and interpretations than conventional scientific experiments. Collaboration can be used to articulate and potentially resolve these differing perspectives [ 14].

Fourth, supporting mobility during inquiry can be expected to be more important than in many conventional inquiry learning contexts. Personal inquiries are expected to span formal and informal settings including the classroom, home, and field locations. Transformative process of personal inquiry therefore need to be carried out and maintained across a range of environments. These environments may suit different kinds of hardware support (e.g., smartphone, netbook, desktop computer) and include locations in which network connectivity cannot be guaranteed [ 15].

The rest of this paper is structured as follows: Section 2 outlines related work in inquiry learning, learning design, and scripting. Section 3 describes the design aims for nQuire. Section 4 presents the main features of nQuire and Section 5 discusses examples of its use in practice. This is followed by a summary and outline of future work.

## Related Work

Inquiry learning is a process by which learning results from designing and carrying out investigations. As described in the previous section, four challenges for personal inquiry learning have been identified in the form of regulatory processes, transformative processes, collaboraiton and sharing, and mobility.

In terms of regulatory processes, previous research has shown that differentiating and articulating the phases of inquiry can assist learners in orienting themselves and monitoring their progress [ 13]. A number of formulations of the inquiry learning process have been proposed. Bruce and Bishop [ 16], drawing on the work of Dewey [ 1], propose a cyclic model of inquiry in which the learner should progress through the phases of asking, investigating, creating, discussing, and reflecting. The learner's reflections may lead them to ask a new question and perform a further cycle of inquiry. White and Frederiksen [ 2] also propose a cyclic inquiry model comprising five processes: question, predict, experiment, model, and apply. Their model was used within the ThinkerTools curriculum to guide students in learning Newtonian models of force and motion using a computer simulation. Co-Lab [ 12], also developed to guide inquiry learning from a computer simulation, identifies five inquiry phases: analysis, hypothesis generation, experiment design, data interpretation, and conclusion. Within the PI project, Scanlon et al. [ 17] propose an eight phase octagonal model of inquiry that also introduces inquiry phases related to initial topic selection, communication of findings, and reflection upon the method of inquiry.

Variations among these proposed models of inquiry originate from their intended contexts of use, such as whether the inquiry is being conducted in a real or simulated environment, the extent to which the domain and methods of inquiry can be manipulated by the learner, and the importance given to the communication and presentation of findings. These variations suggest that there cannot be a single formulation of inquiry learning appropriate across all contexts.

Recent research indicates ways in which transformative proceses can be supported. For example, the LETS GO project focusses on supporting the collection of data using sensors and its visualization and interpretation [ 18]. The software, running on Windows smartphones, can be used to integrate sensor streams with time and location information. Web APIs (from, e.g., Google Maps) can then be used to construct map and timeline visualizations of the data to support the learner's analysis.

The Science Created by You (SCY) project aims to develop new approaches to inquiry learning in science for students aged 12 to 18 years [ 19]. A key concept of SCY is Emerging Learning Objects that are created and shared by learners in the process of inquiry. ELOs can essentially be thought of as accumulating the products of transformative processes during inquiry. Within the SCY project learning scenarios students work individually or collaboratively on “missions” such as “how can we produce healthier milk?” that draw on knowledge from different domains, such as mathematics, biology, and engineering.

The work of Zhang et al. [ 20] and Looi et al. [ 21] illustrates how mobility in inquiry learning can be supported, specifically in the form of a mobile inquiry learning curriculum for primary science. During its design, they took as their starting point an existing school science curriculum and “mobilized” it by analyzing its existing learning objectives and constructing an alternative, drawing on the affordances of mobile technology. Students were provided with 1:1 ownership and 24/7 availability of a networked Smartphone device for the duration of a school year. The devices were installed with proprietary learning management software that allowed the student to manage learning goals by specifying what they know, what they want to know, and what they have learned. Integrated software tools were provided for activities such as drawing and concept mapping. Students participating in the mobilized curriculum were found to perform better in assessment and foster a positive attitude to mobile learning [ 21].

Collaboration has been found to support the inquiry learning process by allowing learners to draw on the inquiry skills and domain knowledge of their fellows as an additional cognitive tool [ 22]. In collaborative settings, learners can also benefit from accessing and building upon the varied opinions of a group of learners [ 23]. Scripts have been found to be an effective method of designing the types of collaborative interaction can be expected to lead to successful learning outcomes, such as argumentation, explanation, and conflict resolution [ 24]. Within the PI project we built on the notion of a script to investigate how inquiries can be authored by a teacher or learning designer as well as support collaborative learning activities.

Classic examples of scripts include the Jigsaw script in which each group member has a subset of the required information [ 25] and Arguegraph in which students providing conflicting responses to a quiz are selected to work together and attempt to reach consensus [ 14]. A script is made up of a set of phases [ 26]. Each phase has a task, a group of learners, a specification of how learners are distributed (e.g., who does what, who has what information) a mode of interaction (e.g., synchronous or asynchronous) and a time specification.

Dillenbourg and Tchounikine [ 27] distinguish between micro-scripts and macroscripts. A macroscript specifies a pattern of collaboration enacted in order to produce a desired interaction among the participants. A macroscript could be represented in terms of phases and their key characteristics. Most collaboration scripts work by placing an additional burden on “natural” collaboration. The desired interactions have to be undertaken by the participants in order to compensate for this. For example, in the Jigsaw script, required information is distributed, placing an additional burden on participants to explain what they know in order to complete the task. This design principle is known as Split Where the Interaction Should Happen (SWISH) [ 28]. A macroscript can be contrasted with a microscript that defines the finer-grained sequencing of operations such as turn-taking behavior within a dialogue.

Computer support for scripted collaboration needs to be sufficiently flexible to enact the script. Dillenbourg and Tchounikine [ 27] identify two types of constraint that technology can bring to a collaborative context: intrinsic and extrinsic. Intrinsic constraints are a necessary part of the pedagogy, such as limiting each participants' access to information at the start of a Jigsaw script. Extrinsic constraints are due to arbitrary design decisions or limitations of the technology. Software support needs to allow for sufficient flexibility in order to deal with extrinsic constraints. Teachers need flexibility over how the generic script is instantiated and students need freedom over how the task is done so long as the intrinsic constraints are met.

Evidence of the need for this flexibility can be found in systems developed to guide the inquiry process. Later versions of the WISE system [ 29] for planning and carrying out inquiry learning activities allowed for task order not to be strictly defined, facilitating student agency when order was not a pedagogical constraint. Tsovaltzi et al. [ 30] report on scripting support for scientific inquiry that was found to be too inflexible, not allowing access to prior inquiry phases for review purposes. Therefore, there is a need to allow for inquiries to be specified and presented in a way that does not impose unnecessary constraints and that leaves appropriate options open to the learner.

As well as the potential for introducing extrinsic constraints, the formal specification of a runnable script raises further issues concerned with the expressivity of the notation used to define the script and potential mismatches with how the script is conceptualized. Miao et al. [ 31] identify a number of difficulties with the formal specification of collaboration scripts, including modeling changes in groups over time and modeling artefacts that are created by groups of learners and then shared across activities. Weinbrenner et al. [ 32] discuss their ongoing work in developing ontology support for the formal specification of inquiries in the SCY project. A key feature they identify as not being supported by existing authoring or scripting formalisms is the Emerging Learning Object (see previously), which bears similarities to Miao et al.'s [ 31] notion of learner artefacts shared across activities. This problem also has parallels with previous observations as to the difficulties in specifying data flow in learning design and in particular the passing of student work across activities [ 33], [ 34]. A particular challenge in the design of nQuire was how to model and support the flow of data or transformation of objects inherent in inquiry learning, for example, from inquiry questions, to experimental design through to interpretations and conclusions.

## Design Aims

As we aim to support inquiries that are personally relevant, the nQuire software cannot just contain a repository of prepared, off-the-shelf, textbook inquiries but rather needs to provide functionality for the scripting and flexible implementation of inquiries accessible by teachers, learning designers, and even students. Teachers can be expected to use functionality for authoring new inquiry structures, monitoring the state of the inquiry for individual or groups of students, and orchestration (i.e., specifying changes to the inquiry state during runtime). Students need functionality for monitoring and carrying out their inquiries and also configuring inquiries such as deciding what to measure and how to analyze the results. However, specific learning contexts may require the teacher to claim or concede more control over the inquiry script. Some examples are presented in Section 5. It is also important that this functionality is provided in a way that is flexible and minimizes external constraints, for example, by not unnecessarily restricting learner choice.

We know from prior work in inquiry learning that the authored scripts need to support both regulatory and transformative processes, especially as aspects of the inquiry may be carried out in an informal context without available teacher support. The personal inquiry context also necessitates support for both individual and collaborative work on inquiries and the sharing of results. Finally, the software needs to support mobility across learning contexts including the home and school.

In summary, the overall design aims for nQuire are to provide scripting support for personal inquiry learning (for authoring, orchestration, monitoring, configuring, and carrying out inquires) that encompasses regulatory processes, transformative processes, collaboration and mobility.

## nQuire

In this section, we provide an overview of nQuire and how it supports each of these four aspects. nQuire is built on the PHP-based Drupal open source content management system [ 35]. Drupal provides in-built support for handling web forms, content presentation, managing users and groups, and storing and presenting media. A series of additional nQuire modules build on this functionality. These can be divided into three types. A set of core nQuire modules provide functionality for scripting, storing, navigating, and running inquiries. These are a required part of any nQuire installation. A set of activity modules support specific inquiry activities such as data collection, analysis, voting, and uploading presentations. Existing Drupal modules, such as a voting module, can be integrated for use as nQuire activities. Finally, a set of utility modules offer additional functionality such as import, export, and synchronization of inquiries.

### 4.1. nQuire Interface

A typical nQuire homepage screen for an inquiry is shown in Fig. 1. A learner would reach this page by logging in and selecting from their available inquiries. The homepage provides an overview of the phases of the inquiry. The one shown in the figure adopts the cyclic, octagonal representation of the inquiry process developed in the PI project [ 17]. By selecting one of the phases, the learner enters a more detailed view of the inquiry, such as the one shown in Fig. 2. The representation of the overview of inquiry phases is shown top left. This functions as a link back to the inquiry homepage as well as a visual reminder of the inquiry process.

Figure    Fig. 1. Homepage for a microclimates inquiry showing eight inquiry phases visually represented as a cycle.

Figure    Fig. 2. A “Decide by question or hypothesis” phase with the “My hypothesis” activity being carried out.

The navigation panel on the left provides a linearized view of the phase diagram where each phase can be opened, like a file browser, to reveal and link to the constituent activities. Thus, it functions as a dynamic “to do list” for the user. The current activity (in this case to enter a hypothesis for the inquiry) is displayed in the main area of the screen. The “My progress” bar in the banner at the top represents the temporal stages of the inquiry which could, for example, correspond to school lessons or homework assignments. The current temporal stage (in this case, stage 1) is shown in bold. In Figs. 1 and 2, three phases of the inquiry are marked with an asterisk. This indicates that each of these phases contains one or more activities that should be undertaken during this stage, in our terminology they are “focal” to this stage.

### 4.2 Regulatory Processes

Regulatory processes are concerned with managing and understanding flow through the learning activities. For the teacher, this generally involves scripting learning flow, making adaptations during the running of scripts and monitoring progress through the script. For the student, this involves understanding their current state within the script, reviewing progress so far and planning future activities. Within our approach, each scripted inquiry is comprised of activities. As we have seen in the interface above, activities are organized according to phases of the inquiry. The number of phases and their names is specified in the script. Scripting the inquiry is via a forms-based interface. Fig. 3 shows a form for specifying a phase within an inquiry. The visual layout of the phases for a particular inquiry can also be specified. Currently, this can be either a cyclic, list, or tabular representation.

Figure    Fig. 3. Specifying a new phase for an inquiry.

The authoring environment provides an interface for viewing and modifying how the activities are structured by the phases and stages of the inquiry ( Fig. 4). In the first column, the activities are organized into phases. Subsequent columns represent the stages of the inquiry with an “edit” link, indicting that the activity represented by that row is focal for that particular stage. The stage and phase structure shown in Fig. 4 specifies a linear progression through the phases. The activities of the first three phases are carried out in the first “Preparation” stage. The activities of the “Collect my data” phase are carried out in the second “Run” stage. Activities of the remaining stages are carried out in the “Conclude” stage. The structure of the inquiry may specify activities as being focal for any number of the stages. The inquiry structure may also specify different activities of the same phase being focal in different stages.

Figure    Fig. 4. Defining the phases and stage of an inquiry.

The activities contained within the inquiry can, at any time, have one of four statuses: start, edit, view, or unavailable. The status of an activity is represented to the learner in the interface using one of the icons shown in Table 1. Each activity has a predefined status at the start of the inquiry. For example, some teacher-prepared instructions could have a status of “view,” some initial activities may have a status of “start” and others may be unavailable. There are three ways in which the status of an activity can change during the running of an inquiry: when an activity is started, when the inquiry stage is changed, and manually by the teacher.

Table 1. The Types of Status for an Activity

First, the inquiry script, as well as giving the initial status for each activity, specifies for each type of activity how its status changes once it has been started. Generally, an activity can be expected to move to edit status once started. For example, a student might start their hypothesis activity by entering an initial version of it. The hypothesis then changes automatically to edit status and can be modified by the student later.

Second, the status of the activity may change depending on whether it is focal for the current stage. When the teacher changes the inquiry stage (e.g., from lesson one to lesson two), the status of activities is updated depending on whether they are focal within the new stage. By default, activities have a status of “start” or “edit” when focal for a stage and “view” or “unavailable” when not, with edit-view and start-unavailable being paired states. This naturally distinguishes between doing and reviewing the activities of an inquiry, allowing teachers to maintain access to nonfocal activities for review by students while undertaking their current activities.

The third way in which the status of an activity can be changed is manually by the teacher. For example, the teacher may specify a data collection stage in which data points can be added and edited (e.g., while on a field trip). When moving onto the data analysis stage, the teacher may choose to keep the data editable (though not in focus) in order that the student can fix errors in the data spotted during analysis. Fig. 5 shows the interface available to the teacher to change stage during the running of the inquiry. The teacher can select a single stage (from the pull down menu) as being current. The teacher can also choose to keep additional stages within focus. This is part of the intended flexibility of the system and allows the teacher to open multiple stages of the inquiry to allow some students to catch up or move ahead within the inquiry. By selecting on an activity, the teacher can also see the status of each participant (whether an individual or group of students) and manually change their status.

Figure    Fig. 5. Setting the current stage and other focal stages of the inquiry.

In summary, the nQuire approach to scripting regulatory processes allows the inquiry phases, their presentation and the temporal stages to be defined. Activities are organized according to stages and phases though there are opportunities to improvise around this by, for example, keeping multiple stages in focus and manually changing access for particular students. Scripting support for focal and nonfocal activities recognizes the importance of allowing students to review and orient themselves within the inquiry process as well as highlighting access to current activities.

### 4.3 Transformative Processes

Activities are used to generate artefacts during the inquiry, such as notes, questions, data, graphs, and presentations. There are two main types of activities. First, there are generic activities such as making notes, sharing a presentation, or uploading media related to the topic. Second, there are activities specifically related to inquiry such as data collection and interpretation—in line with Quintana et al.'s [ 13] “basic operations of scientific inquiry.” The generic activities create artefacts, such as presentations and notes, but are stand-alone, and do not automatically pass content to other activities. The inquiry-related activities share data according to an underlying data model of the inquiry process. This allows, for example, for the selection of measures when designing the investigation to configure data collection activities in order to reflect the choice made by the student. Similarly, the model allows the results of data collection activities to be made in available in an appropriate form within data analysis activities. Similar to Roschelle et al.'s [ 36] T-Spaces, it provides a shared memory across activities, though with a structure specialized for inquiry learning.

The underlying data model is shown in Fig. 6. Each inquiry may use a hypothesis activity that the learner can use to create, edit, and view their hypothesis. A single conclusion can be associated with the hypothesis. Each hypothesis can be associated with a number of key questions. These can be used to help the student to break down and operationalize their hypothesis. Each key question can be associated with a number of key answers.

Figure    Fig. 6. The data model supporting transformative processes.

A personal inquiry is defined by selecting and organizing measures made available to the inquiry. Available measures could be sensor readings, interview questions, image upload, audio recordings, et cetera. Each available measure has a data type (e.g., number, text, list of options) and a mode of presentation (e.g., single or multiline text box, pull down menu, file upload dialogue). For available measures that are a list of options, additional data can be associated with each option. For example, if the options are a list of locations at which climate readings can be taken, then GPS location data could be associated with each option. This would allow the students to visualize the data on a map without entering coordinate information themselves. Similarly, if the inquiry involved selecting foodstuffs that had been eaten (e.g., cereal, apple, cola, etc.) as part of a dietary analysis, each food item could be associated (manually by the teacher or from a database) with information on calories or nutrient intake, without this data having to be entered by the student. This additional information could be summed and presented during data collection and analysis.

Available measures can be created by the teacher or student (this will be discussed in Section 5). The same measures may be used in many student inquiries. Fig. 7 shows the interface of the activity for selecting measures. Some of the selected measures can be defined as key measures. Operationally, these are essentially the independent variables of the investigation (though the assumption that they are controlled by the experimenter need not necessarily hold) in that they are used to organize the data and used as $x$ -axes when generating charts of the data. An example of this is shown later when discussing how results presentations are generated.

Figure    Fig. 7. Selecting from the available measures of the inquiry.

Data collection activities are structured according to the selected measures. In the example data collection activity shown in Fig. 8, the measures and their order reflects the choice made in the selected measures activity shown in Fig. 7. This has similarities to the approach described by Giemza et al. [ 37] in which they provide an authoring tool that is used to generate a data collection form. However, our approach allows the definition of individual measures to be saved as available measures and reused across inquiries.

Figure    Fig. 8. A data entry activity.

By default, the set of collected data items for an inquiry are presented in a sortable list, each data entry having a title reflecting its values for the one or more key measures of the inquiry ( Fig. 9). For many inquiries, one data entry activity may be expected for any combination of values for the key measures, for example, a set of dependent measures taken at each of a number of times and/or locations.

Figure    Fig. 9. A generic data management activity.

For some inquiry designs, multiple data entries may be collected for the same combination of key measure values. For example, a healthy eating inquiry may have date and meal as key measures and record a number of food items and their quantity. nQuire can provide some custom views for entering and navigating this data. Fig. 10 shows the custom data collection interface used for a healthy eating inquiry in which the ingredients of each meal over the entire day can be specified. Additional information associated with the selected food items automatically populates the dietary information table shown at the bottom of the figure.

Figure    Fig. 10. A custom data management activity used for a food diary inquiry.

Presentations of the results can be generated by specifying and ordering a subset of the selected measures. Each results presentation is associated with a key question. Fig. 11 shows a results presentation charting temperature across locations. It has been associated with the key question “How will temperature vary across the locations?” As location was previously specified as a key measure and the temperature measure is defined as numeric data, the results presentation activity automatically provides a bar chart of the results with location on the $x$ -axis and temperature on the $y$ -axis.

Figure    Fig. 11. A results presentation activity.

If coordinate information is included in the key measures, then the results are also provided in a KML format that can be visualized in Google Earth. Presentation of results can also provide data comparisons. For a healthy eating investigation, for example, the student daily nutrient intake is compared to the recommended nutrient intake suggested by the UK Food Standards Agency. Data activities also provide support for data import as well as export. nQuire can be used in conjunction with Sciencescope dataloggers [ 38] to automatically add data into the inquiry.

Where possible, the set of inquiry-related activities can still be used when other activities with which they have potential dependencies are omitted. Inquiries can and have been used that omit the key questions and key answers, just having a hypothesis and conclusion. In this case, the result presentation activity can still be used, but there is no option to associate each set of results with a key question.

In summary, nQuire contains an extensible set of activities that can be used within inquiry scripts. A set of activities directly related to the inquiry process automatically share data. This facilitates the transformative process of the inquiry from hypothesis, though data collection and analysis, to conclusion. Configuring an inquiry, by adding new available measures and using them to design the inquiry, makes it possible for students to design and carry out a broad range of inquiries within the same overall script. This support for scripting and configuring transformative processes provides teachers and students agency and frees them from an off-the-peg set of inquiries.

### 4.4. Collaboration and Sharing

nQuire provides support for both working individually and within defined collaborative structures. When defining collaborative learning activities in schools, particularly in terms of collaboration scripts, three social planes on which activity takes place can be identified: individual, group, and class [ 28] in which groups are a distinct subdivision of the class. Groups and their membership can be expected change over time, for example, to fit particular group-based activities or in response to student absences from class.

Within nQuire, collaboration is scripted according to these three social planes. The script defines for each activity who can carry it out and who will be able to access the result. This information is displayed to the user whenever they are starting or editing an activity, using messages in the style “Being done by my group. Will be seen by my class.” The example data entry activity shown in Fig. 8 is both performed and seen on the group level. The group of participants accessing the results can be the same or broader than the group who carried out the activity. For example, an individual can carry out an activity (such as entering a piece of data) which then becomes available to his or her group. However, a group cannot collaboratively enter data that is then only accessible to a member of that group.

With generic activities, such as adding a presentation, this functionality can be used to create and publish information within the class. For example, an activity could contain a presentation that can only be created and edited by the teacher but is accessible to the whole class. Similarly, each group could make a presentation about their inquiry that is then visible to the whole class.

When used in conjunction with the inquiry-specific activities (such as selecting measures, and collecting and analyzing data), the three social planes can be used to create different distributions of activities across the inquiry, such as the one shown in Fig. 12. Here, deciding on a hypothesis and data analysis are done individually, data are collected in groups and experimental design is done on a class level. In this case, each group collects data according to the design specified on the class level. Each student then analyzes their group's data individually. Alternatively, activities concerned with defining and choosing measures could be specified on the group level. In this case, each group would be designing and running their own experiment. More examples of inquiries differing in terms of the social planes on which activities are carried out can be found in Section 5.

Figure    Fig. 12. Example activity distribution used for a microclimates inquiry.

The majority of trials we have conduced with nQuire have involved relatively stable group compositions throughout the inquiry, in which all activities carried out on the group social plane are constituted using the same participant groups. This preference for a more stable group structure originated from the teachers. However, nQuire also allows different group compositions (called groupings) to be used in different parts of the inquiry. The part of the inquiry might be a particular temporal stage, inquiry phase or activity.

This can be used to script SWISH-style collaborative patterns [ 28]. As described in Section 2, membership changes in successive group configurations can be used to engineer splits, in, for example, goals or background knowledge of group members. These splits require collaboration in order to be resolved. Similar patterns can be embedded within nQuire scripts. For example, in the first stage of the inquiry, students could be given different background materials to read depending on group composition. Different materials may suggest alternative inquiry methods or questions. In a second stage, in which the students design the inquiry, an alternative grouping of students could be used. Each new group would have to reach a consensus across the background materials presented. Similarly, the script could be used to regroup students between data collection and analysis, this partial rotation of students bringing a fresh perspective to the data during the analysis phase.

In summary, nQuire scripts can be used to represent activities on three social planes: individual, group, and class. The script can distinguish the plane on which the activity is carried out from the plane on which the result of the activity is viewed. This supports information sharing and publishing among inquiry participants. When used in conjunction with transformative processes, the script can be used to create different patterns of collaboration across the inquiry. Scripting different groupings allows personal inquiry equivalents of many collaboration scripts to be authored.

### 4.5 Mobility

nQuire offers an approach to mobility though does not necessarily require constant connectivity. Our experiences, reported by Gaved et al. [ 15], indicate the need to have alternative ways of operationalizing the inquiry in terms of devices and connectivity and the ability to flexibly move between these depending on circumstances (e.g., unavailability of a network).

Within the trials of the PI project, we used three different kinds of infrastructure. First, when internet connectivity could be reasonably guaranteed, such as in the classroom or in the homes of some students, mobile devices, such as netbooks, or desktop PCs were used as clients to assess the inquiry from a central server. This approach was generally used during the analysis and writing up phases of the larger inquiries which were generally classroom based. Second, when participants were distributed but did not need to synchronously share information across devices, the local installation of nQuire was used on netbooks, and then later synchronized with the central server. This approach was used for collecting environmental data across various field locations. Third, a mobile server with wireless connectivity was used for some field activities in which all students were contained within a reasonably small and open area. This third approach allows intranet connectivity between the participants (e.g., allowing them to see each other's data) using mobile devices as web clients. The mobile server is then synchronized with the central server after data has been collected.

During trials, this range of options allowed us to improvise and continue when unexpected networking problems arose. This included continuing to work on an inquiry on the school premises when a power cut prevented connectivity through the school network. The interface snapshots in the previous figures show the nQure interface design optimized for desktops and netbooks. nQuire also has a custom mobile interface with optimized navigation and layout. This can be used, for example, when collecting observational data using a smartphone.

## UsingnQuire to Support Personal Inquiry

nQuire was developed through a process of design-based research [ 39], [ 40] in which our conceptualization of personal inquiry learning and how it should be supported motivated design decisions in nQuire which were then tested in school-based trials. In total, seven major trials were conducted over a 2.5 year period. Six teachers and over 500 students participated in the trials. These covered a range of inquiries within the science and geography disciplines such as microclimates, healthy eating, bird feeding, and sustainability. The first two trials focussed on data collection and analysis activities and allowed us to develop and test the presentation and navigation of activities, the design of forms, data entry, and the presentation of results in tables, charts, and on maps. The subsequent trials focussed on developing the inquiry structure, in terms of phases and stages and using this for interface structure and navigation. The final trials introduced the underlying data model and the broader range of activities having dependencies to this model.

In all, three main approaches to the authoring and configuration of scripts have been used across these trials. These differ in terms of the level of agency they provide to the students participating in the inquiries. These approaches illustrate ways in which the software provides an approach to the scripting and configuration of inquiries that is accessible to teachers and students. Further details on the trials as well as an online demo and software download of nQuire are available from http://www.nquire.org.uk.

### 5.1. Teacher-Led Personal Inquiry

nQuire has been used to script inquiries in participation with the teacher. Here, the structure of the inquiry was specified in terms of stages, phases, and activities. Activities concerned with experimental design (i.e., the defining and selection of measures) were then used by the teacher to configure the inquiry design on behalf of the class. The inquiry was then conducted by the class. In this context, nQuire is being used by the teacher to completely script the inquiry for the whole class. This approach was used in an urban heat islands inquiry which involved data collection by students walking across the centers of two towns [ 6], [ 41]. It was also used in a healthy eating inquiry that involved students collecting data about their eating habits at home or elsewhere [ 7]. The structure of these inquiries and the experimental design were scripted in participation with the teachers. The prepared structure was then used to guide the students through data collection and analysis.

The urban heat islands study was conducted for two consecutive academic years with 78 and 57 students, respectively. An urban heat island is a metropolitan area that is warmer than the surrounding countryside. The aim of the inquiry was to investigate whether the urban heat island effect differed in the student's hometown (a new town with a relatively low density and green town center) compared to a nearby conventional town of comparative size.

nQuire was initially used by the students to record their hypothesis. These generally took the form of a prediction as to whether they expected a difference between the two towns and what form that difference would take. Key questions were specified that operationalized the hypothesis in terms of the data to be collected, for example, predicted differences in temperature or carbon monoxide level across the two towns.

Data collection took a full school day and involved collecting environmental data and related observations at points across the two towns. Location was measured using a GPS device. A camera was used for taking photographs. All data were recorded manually in nQuire installed on a netbook. Data collection was carried out in groups of four or five with each group member taking a different role such as temperature measurement or data entry. Generally, roles were circulated a number of times during the day.

After the field trip, data were synchronized from the netbooks to an installation of nQuire on a central server and photographs were uploaded. Back in the classroom, students then worked individually for the rest of the inquiry. Data were initially checked. Selections of the data were then made around each key question. The entire data table or data for specific questions were exported for visualization on GoogleEarth or further analysis in a spreadsheet. Answers were recorded for each of the key questions. The hypothesis, data, questions, and answers recorded in nQuire were used as a resource to assist in the writing of their reports during the following few weeks.

Drawing on observational data and posttrial interviews with students and teachers, nQuire was found to assist students in building connections between phases of the inquiry and encourage the revisiting and refining earlier inquiry activities such as initial notes on the topic and inquiry questions [ 41].

### 5.2. Teacher-Led Personal Inquiry with Student Choice

nQuire has been used to script the inquiry structure and prespecify available measures and hypotheses from which the students themselves can select. Choices made by the students individually and in groups and key questions added by the student are used to structure their activities concerned with data collection, presentation, and concluding their inquiry. In this context, the script is being used to scope the inquiry and provide the student with options that guide their later activities. This approach was used for a microclimate inquiry around the school grounds. The aim of the inquiry was to determine where in the school grounds would be the optimal location for a particular activity such as flying a kite, having a picnic, or planting a garden. The nQuire script was preauthored with a number of possible hypotheses and available measures from which the students could select.

The microclimates inquiry was carried out with younger students around 12 years of age and spanned three geography lessons. During the first lesson, the students chose their hypothesis and measures in small groups. In the second lesson, they collected their required data in the school grounds. For this, the groups were given a netbook for data recording and any required scientific sensors such as light sensors and anemometers. A wireless network was set up in the school grounds so that students could access nQuire from a central server using their netbook. In the third lesson, students worked individually on data analysis accessing nQuire using a netbook or computer in the school IT suite.

Posttrial interviews with students and teachers revealed that nQuire helped students to capture their decisions and understand their implications for future inquiry activities [ 42]. For example, students could select measures and locations, look ahead to see the associated data collection plan and then alter their design again if they wished.

### 5.3. Student-Led Personal Inquiry

We have run more open-ended inquiries in which the students could pick their own topic, design their own experiment, and collect and interpret their own data. These were carried out as part of an after-school geography club around the topic of sustainability [ 5]. The after-school club was run weekly over one term. Attendance varied from week to week with a maximum of about 30 students. A number of inquiries were carried out during the club. The inquiry with the longest timespan was carried out in groups of around four and investigated how factors such as price and packaging affected the shelf life of different foods. The group observed during the evaluation compared fair trade and value bananas with and without packaging. They measured temperature and took photographs a few times per day to determine how quickly they rotted. The experiments were conducted at home and the data discussed at the weekly club. At the end of the inquiry, the students presented a poster of their findings to the wider group.

To support these student-led inquiries, nQuire was used by the students, with the assistance of a researcher, to define measures (such as scales for measuring odor and discoloration) and then select these for use within the inquiry. Posttrial interviews with the teachers, students, and their parents showed that students were motivated by being able to design and carry out inquiries of their own choice, and that activities in the club had encouraged them to think carefully about, and change, their own food purchasing behaviors [ 5].

## Conclusions and Future Work

nQuire provides an approach to scripting personal inquiry learning, with a particular emphasis on regulatory processes, transformative processes, collaboration, and mobility. nQuire allows inquiries to be scripted and configured in various ways, in order that personally relevant, rather than off-the-shelf inquiries can be created and used by teachers and students. An important aim of nQuire was to design for flexibility whether through providing access to nonfocal activities for review, allowing teachers and students to configure inquiries, or to improvise when network connectivity fails.

Scripting is via a conventional forms-based interface and makes use of a set of constructs such as phases, stages, activities, activity status, and groupings. These constructs are relatively atomic and focus on the mechanics of the script. A valuable extension to nQuire would be to devise a way of representing scripts abstracted from the operational level and more focussed on explicating the pedagogical objectives of the script. Drawing on the distinction made in Section 2, this new representation could essentially be thought of as a macroscript [ 27], framing the current nQuire constructs as the language of a microscript for personal inquiry learning.

The nQuire macroscript could be used in three ways. First, it could be used to provide authoring support for teachers. This would allow the script to be partially specified pedagogically, with further implementation decisions taken on the microscripting level. This would bear certain similarities to the Domain Specific Modeling (DSM) approach proposed by Miao et al. [ 43]; however, the motivation would be to provide two complementary vocabularies appropriate for expressing pedagogical and operational concerns rather than because the operational level was necessarily inaccessible to end users. Currently, nQuire can provide this pedagogical layer to some extent through a set of reusable inquiry scripts that exemplify particular pedagogical decisions, though these decisions are not currently explicit within the script.

Second, the macroscript could be used to help the monitoring of inquiries. Currently, the teacher can use tools such as the activity monitor to see the status of activities and well as viewing the content of student work. Allowing the teacher to easily visualize during runtime interesting patterns in student behavior on the macroscript level would allow them to identify and respond to pedagogically significant states. These patterns could be in the state or interaction history of a single student or across a number of students in the class. These could be difficult to identify in-situ from atomic components of the inquiry.

Third, the macroscript could assist improvisational, contingent orchestration [ 44] during the running of an inquiry by not only assisting in the identification of interesting patterns but also in making pedagogically meaningful responses. Often these patterns can be expected to signify breakdowns in the inquiry process that the additional macroscript component would be intended to remedy. This can be thought of as the converse of the SWISH-style script [ 28] in which a conflict is engineered. As we have witnessed during the extensive trials of the project, personal inquiry learning scenarios carried out in real-world settings can often generate their own conflicts. Examples include different students taking wildly different readings in the same context or making conflicting interpretations of the same data. Personal inquiry macroscripts could be used to identify and turn these situations into pedagogically constructive events. For example, two groups of students with wildly differing data could have a macroscript component added to their inquiry that guides them through investigating differences in how their data was collected.

A pedagogical user community is currently being built around the current nQuire software and lesson plans. This community could allow us to start to explore the value of personal inquiry macroscripts for authoring, monitoring, and orchestration.

## Acknowledgments

The Personal Inquiry project was funded by the UK EPSRC and ESRC Research Councils under the TEL-TLRP programme. The authors would like to thank the staff and students of Oakgrove School, Milton Keynes and Hadden Park School, Nottingham for their participation. They would also like to thank Sciencescope for their advice and support.

## References

• 1. J. Dewey, How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. Heath, 1933.
• 2. B. White, and J. Frederiksen, “Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students,” Cognition and Instruction, vol. 16, no. 1, pp. 3-118, 1998.
• 3. M. Linn, D. Clark, and J. Slotta, “WISE Design for Knowledge Integration,” Science Education, vol. 87, no. 4, pp. 517-538, 2003.
• 4. L. Kerawalla, K. Littleton, E. Scanlon, A. Jones, M. Gaved, T. Collins, P. Mulholland, C. Blake, G. Clough, G. Conole, and M. Petrou, “Personal Inquiry Learning Trajectories in Geography: Technological Support across Contexts,” Interactive Learning Environments, in press.
• 5. E. Scanlon, L. Kerawalla, M. Gaved, A. Jones, T. Collins, P. Mulholland, C. Blake, M. Petrou, and K. Littleton, “The Challenge of Supporting Networked Personal Inquiry Learning across the Contexts of School Club and Home,” Proc. Seventh Int'l Conf. Networked Learning, 2010.
• 6. T. Collins, M. Gaved, P. Mulholland, L. Kerawalla, A. Twiner, E. Scanlon, A. Jones, K. Littleton, and G. Conole, “Supporting Location-Based Inquiry Learning Across School, Field and Home Contexts,” Proc. mLearn Conf., 2008.
• 7. S. Anastopoulou, M. Sharples, S. Ainsworth, C. Crook, C. O'Malley, and M. Wright, “Personalising Inquiry Learning,” Int'l J. Science Education, in press.
• 8. S. Anastopoulou, Y. Yang, M. Paxton, M. Sharples, C. Crook, S. Ainsworth, and C. O'Malley, “Maintaining Continuity of Inquiry Learning Experiences across Contexts: Teacher's Management Strategies and The Role of Technology,” Sustaining TEL: From Innovation To Learning And Practice, M. Wolpers, P. Kirschner, M. Scheffel, S. Lindstaedt, V. Dimitrova, eds., Springer-Verlag, 2010.
• 9. E. Scanlon, K. Littleton, S. Anastopoulou, S. Ainsworth, and PI Project Team, “Personal Inquiry and Groupwork: Issues for Computer-Supported Inquiry Learning,” Proc. Symp. Issues in Scaffolding Collaborative Inquiry Science Learning in Computer Supported Collaborative Learning (CSCL '09), p. 35, 2009.
• 10. T. de Jong, “Computer Simulations—Technological Advances in Inquiry Learning,” Science, vol. 312, pp. 532-533, 2006.
• 11. L. Kobbe, A. Weinberger, P. Dillenbourg, A. Harrer, R. Hamalainen, P. Hakkinen, and F. Fischer, “Specifying Computer-Supported Collaboration Scripts,” Int'l J. Computer-Supported Collaborative Learning, vol. 2, pp. 211-224, 2007.
• 12. W. van Joolingen, T. de Jong, A. Lazonder, E. Savelsbergh, and S. Manlove, “Co-Lab: Research and Development of an Online Learning Environment for Collaborative Scientific Discovery Learning,” Computers in Human Behaviour, vol. 21, pp. 671-688, 2005.
• 13. C. Quintana, B.J. Reiser, E.A. Davis, J. Krajcik, E. Fretz, R.G. Duncan, E. Kyza, D. Edelson, and E. Soloway, “A Scaffolding Design Framework for Software to Support Science Inquiry,” J. Learning Sciences, vol. 13, no. 3, pp. 337-386, 2004.
• 14. P. Jermann, and P. Dillenbourg, “An Analysis of Learner Arguments in a Collective Learning Environment,” Proc. Conf. Computer-Supported Collaborative Learning, 1999.
• 15. M. Gaved, P. Mulholland, L. Kerawalla, T. Collins, and E. Scanlon, “More Notspots than Hotspots: Strategies for Undertaking Networked Learning in the Real World,” Proc. Ninth World Conf. Mobile and Contextual Learning (mLearn '10), 2010.
• 16. B.C. Bruce, and A.P. Bishop, “Using the Web to Support Inquiry-Based Literacy Development,” J. Adolescent and Adult Literacy, vol. 45, no. 8, pp. 706-714, 2002.
• 17. E. Scanlon, S. Anastopoulou, L. Kerawalla, and P. Mulholland, “Scripting Personal Inquiry: Using Technology to Represent and Support Students' Understanding of Personal Inquiry across Contexts,” J. Computer-Aided Learning, in press.
• 18. B. Vogel, D. Spikol, A. Kurti, and M. Milrad, “Integrating Mobile, Web and Sensory Technologies to Support Inquiry-Based Science Learning,” Proc. IEEE Int'l Conf. Wireless, Mobile and Ubiquitous Technologies in Education, 2010.
• 19. T. de Jong, W. van Joolingen, A. Weinberger, and the SCY Team, “Learning by Design: An Example from the SCY Project,” Proc. Conf. Computer-Supported Collaborative Learning (CSCL '09), 2009.
• 20. B. Zhang, C.-K. Looi, P. Seow, G. Chia, L.-H. Wong, W. Chen, H.-J. So, E. Soloway, and C. Norris, “Deconstructing and Reconstructing: Transforming Primary Science Learning via a Mobilized Curriculum,” Computers & Education, vol. 55, pp. 1054-1523, 2010.
• 21. C.-K. Looi, B. Zhang, W. Chen, P. Seow, and G. Chia, “Mobile Inquiry Learning Experience for Primary Science Students: A Study of Learning Effectiveness,” J. Computer Assisted Learning, in press.
• 22. W. van Joolingen, T. de Jong, A. Dimitrakopoulou, “Issues in Computer Supported Inquiry Learning in Science,” J. Computer Assisted Learning, vol. 23, pp. 111-119, 2007.
• 23. H. Gijlers, and T. de Jong, “Sharing and Confronting Propositions in Collaborative Inquiry Learning,” Cognition and Instruction vol. 27, no. 3, pp. 239-268, 2009.
• 24. P. Dillenbourg, and F. Fischer, “Basics of Computer-Supported Collaborative Learning,” Zeitschrift für Berufs- und Wirtschaftspädagogik, vol. 21, pp. 111-130, 2007.
• 25. E. Aronson, N. Blaney, C. Stephin, J. Sikes, and M. Snapp, The Jigsaw Classroom. Sage, 1978.
• 26. P. Dillenbourg, “Over-Scripting CSCL: The Risks of Blending Collaborative Learning with Instructional Design,” Three Worlds of CSCL: Can We Support CSCL? P.A. Kirschner, ed., Open Univ. of the Netherlands, 2002.
• 27. P. Dillenbourg, and P. Tchounikine, “Flexibility in Macro-scripts for Computer-Supported Collaborative Learning,” J. Computer Assisted Learning, vol. 23, pp. 1-13, 2006.
• 28. P. Dillenbourg, and F. Hong, “The Mechanics of CSCL Macro Scripts,” Int'l J. Computer-Supported Collaborative Learning, vol. 3, pp. 5-23, 2008.
• 29. M. Linn, and J. Slotta, “WISE Science,” Educational Leadership, vol. 58, no. 2, pp. 29-32, 2000.
• 30. D. Tsovaltzi, N. Rummel, B. McLaren, N. Pinkwart, O. Sheuer, A. Harrer, and I. Braun, “Extending a Virtual Chemistry Laboratory with a Collaboration Script to Promote Conceptual Learning,” Int'l J. Technology Enhanced Learning, vol. 12, nos. 1/2, pp. 91-110, 2010.
• 31. Y. Miao, K. Hoeksema, U. Hoppe, and A. Harrer, “CSCL Scripts: Modelling Features and Potential Use,” Proc. Conf. Computer Support for Collaborative Learning, 2005.
• 32. S. Weinbrenner, J. Engler, L. Bollen, and U. Hoppe, “Ontology-Based Support for Designing Inquiry Learning Scenarios,” Proc. Int'l Workshop Ontologies and Semantic Web for E-Learning (SWEL), 2009.
• 33. J. Dalziel, “Lessons from LAMS for IMS Learning Design,” Proc. Int'l Conf. Advanced Learning Technologies, 2006.
• 34. L. Palomino-Ramirez, A. Martinez-Mones, M. Bote-Lorenzo, J. Asensio-Perez, and Y. Dimitriadis, “Data Flow between Tools: Towards a Composition-Based Solution for Learning Design,” Proc. Int'l Conf. Advanced Learning Technologies, 2007.
• 35. Drupal - Open Source CMS, http://www.drupal.org. 2011.
• 36. J. Roschelle, P. Schank, J. Brecht, D. Tatar, and S.R. Chadhury, “From Response Systems to Distributed Systems for Enhanced Collaborative Learning,” Proc. 13th Int'l Conf. Computers in Education (ICCE '05), 2005.
• 37. A. Giemza, O. Kuntke, and U. Hoppe, “A Mobile Application for Collecting Numerical and Multimedia Data during Experiments and Field Trips in Inquiry Learning,” Proc. Int'l Conf. Computers in Education, 2010.
• 38. Sciencescope, http://www.sciencescope.co.uk, 2012.
• 39. S. Barab, and K. Squire, “Design-Based Research: Putting a Stake in the Ground,” The J. Learning Sciences, vol. 13, no. 125, pp. 1-14, 2004.
• 40. P. Cobb, J. Confrey, A. di Sessa, R. Lehrer, and L. Schaubel, “Design Experiments in Educational Research,” Educational Researcher, vol. 32, no. 126, pp. 9-13, 2003.
• 41. E. Scanlon, K. Littleton, M. Gaved, and L. Kerawalla, “Support for Evidence-Based Inquiry Learning: Teachers, Tools and Phases of Inquiry,” Proc. Biennial Conf. European Assoc. for Research on Learning and Instruction (EARLI '09), 2009.
• 42. L. Kerawalla, K. Littleton, E. Scanlon, A. Jones, M. Gaved, T. Collins, P. Mulholland, C. Blake, G. Clough, G. Conole, and M. Petrou, “Personal Inquiry Learning Trajectories in Geography: Technological Support Across Contexts,” Interactive Learning Environments, in press.
• 43. Y. Miao, T. Sodhi, F. Brouns, P. Sloep, and R. Koper, “Bridging the Gap between Practitioners and E-Learning Standards: A Domain-Specific Modeling Approach,” Proc. European Conf. Technology Enhanced Learning (EC-TEL '08), 2008.
• 44. M. Sharples, and S. Anastopoulou, “Designing Orchestration for Inquiry Learning,” Orchestrating Inquiry Learning: Contemporary Perspectives on Supporting Scientific Inquiry Learning, K. Littleton, E. Scanlon and M. Sharples, eds., Routledge, in press.

Paul Mulholland is a research fellow. Much of his research is concerned with how web and knowledge technologies can be used to support learning in different contexts, whether that be museum settings, informal game playing for children, the workplace, at school, or in higher education.
Stamatina Anastopoulou is a research fellow and she is interested in the design and use of technology to support learning through inquiry and play. Apart from inquiry-based learning, current areas of involvement include game-based learning and fieldwork.
Trevor Collins is a research fellow working in technology-enhanced learning. His research focuses on the application of mobile and network technologies to enable more active forms of learning involving: collaboration, dialogue, and the use of shared representations.
Markus Feisst received the doctoral degree from the University Louis Pasteur Strasbourg, France, in 2006. In December 2009, he started his work in the context of the “nQuire” project at the LSRI at the University of Nottingham. Since September 2010, he has been working in computer science at the University of Nottingham on the EDUCATE European project.
Mark Gaved is a research associate in the Open University Business School. His research interests include networked and informal learning, the use of mobile and sensor technologies to support education, and the appropriation of information technologies by community initiatives.
Lucinda Kerawalla is a lecturer in childhood and youth studies. Her research interests include technology-mediated learning, classroom dialogues, and mobile learning.
Mark Paxton is a research fellow at Horizon Digital Economy Research at the University of Nottingham. His research interests relate to human-sensor interaction, particularly with groups of handheld devices and fixed and mobile networked sensors.
Eileen Scanlon is professor of educational technology and associate director (research and scholarship) in the Institute of Educational Technology and led the Personal Inquiry project at the Open University. Her research interests include technology-enhanced science learning in both formal and informal settings.
Mike Sharples is a professor of educational technology in the Institute of Educational Technology at the Open University, United Kingdom. His research involves human-centered design of new technologies for learning. He inaugurated the mLearn conference series and was the founding president of the International Association for Mobile Learning.
Michael Wright is currently working toward the PhD degree at the University of Bath. His current work is in gestural interaction for ubiquitous computing. Perviously, he was a research associate at the University of Nottingham exploring pervasive games and mobile learning.