Ubiquitious Computing Devices in the Training of Teacher-trainers

In September 2014, the computing curriculum in English schools changed to one with a much greater emphasis on computer science. However, 66% of existing ICT teachers are non-specialist and require significant continuing professional development (CPD) to deliver this new curriculum. One initiative to provide this is the Computing At School (CAS) Master Teacher programme. This paper describes some physical computing projects that were used in training a cohort of Master Teachers, preparing them to deliver both improved lessons in classrooms and CPD tailored for the requirements of their peers. Introduction In September 2014, the computing curriculum in English schools changed to one with a much greater emphasis on computer science, compared to the previous curriculum mainly based on ICT and digital literacy (Department for Education, 2013). Of UK computing school teachers, 66% are non-specialists in computer science who do not have the subject-specific skills or experience to deliver the new curriculum as effectively as they would like (Furber, 2012). One initiative to address this deficit in skill is the Computing At School (CAS) Master Teacher programme (Computing At School, 2015), where technically competent in-service teachers provide professionally relevant professional development to their peers within and across schools. This paper describes our experience with the CAS Master Teaching training programme, where we train the master teachers. The training involves a combination of technical education and training the teachers how to deliver effective continuing professional development (CPD) to adult learners. We employed a blended learning approach, combining face-to-face sessions, online tutorial support, and guided review of example practice by the teachers in delivering CPD. We use various physical computing devices in the training to illuminate key computer science principles, as well as show the trainee master teachers how these devices can be used for CPD delivery and directly in classrooms with children. As well as the use of a variety of robots and physical computing projects, we also use the SenseBoard (see below), a novel device developed by the Open University for supporting ubiquitous computing and internet-ofthings applications. Teaching Approach Training for master teachers is a two-stage process. Level 1 training is focussed on developing subject knowledge expertise (SKE) in the teachers, to ensure they have sufficient mastery of their subject. Level 2 training focusses ICICTE 2015 Proceedings 43 on how to develop and deliver CPD for other teachers. We were providing Level 1 training. Our cohort consisted of ten in-service teachers, split between five primary school teachers and five secondary school teachers. (Primary school covers ages 5 to 11; secondary school covers ages 11-18.) The training provision suggested by Computing At School suggested a blended learning approach. All teachers received five days of face-to-face training plus ten hours of online tutorial support; the secondary school teachers received an additional five days of face-to-face training to cover the additional subject knowledge expertise requirements. The online tutorial support was delivered through a combination of video conferencing using Google Hangouts and email. The face-to-face teaching time was supplemented by the trainees undertaking various activities between the contact periods, where the trainees had to perform various tasks such as preparing sample CPD material and reflecting on what they had learnt and how that could be used to improve their practice. The face-to-face teaching was supplemented by the use of online tools where trainees could develop and share resources created during the training. These resources included learning resources taken from various places online and resources created by the trainees both during the sessions and elsewhere. Ubiquitous and Physical Computing Devices Physical computing has recently been seen as a first step in getting novices and children engaged with computer science (e.g., Buechley, Eisenberg, Catchen, & Crockett, 2008, Lau, Ngai, Chan, & Cheung, 2009, Richards, Petre, & Bandara, 2012, Richards & Smith 2010). The use of physical computing devices has several benefits over a software-only approach to education. The physical device offers a tangible focus of attention for the learner, the use of a playful and interactive device can reduce feelings of insecurity in learners, and the physical device can offer immediate and obvious feedback on progress. As part of the teacher training, we have demonstrated a variety of physical computing devices and outlined their different pedagogic applications for both teaching children and for delivering CPD to teachers (specialist and nonspecialist alike). In the remainder of this section, we outline the devices demonstrated. In the next section, we outline how these devices have been used in schools, both by the trainee master teachers and others. SenseBoards The SenseBoard (Figure 1) is a tethered device based around the Arduino microcontroller. It was developed for novice computer science students working at a distance in the UK's Open University, as part of the module My Digital Life (Richards et al., 2012). We wanted a way of introducing our starting students, many of whom have never before studied any form of computer science, to computing in a gentle and immediate way. The use of physical computing was a natural way to introduce the creative and practical aspects of computer science (Richards & Smith 2010). ICICTE 2015 Proceedings 44 Figure 1. A schematic view of a SenseBoard. The SenseBoard was designed as a ubiquitous computing lab-in-a-box for teaching undergraduate students at a distance. The SenseBoard's robust construction and ease of use make it suitable for use in classrooms. The accompanying Sense programming language (based on Scratch) allows even young children easily to develop internet-aware physical computing devices. The Open University teaching context of solely distance learning leads to some interesting constraints on how a physical computing environment can be delivered. One constraint is that the SenseBoard kit supplied to the students should be self-contained; there is no facility for students to pop into a lab and collect additional equipment for specific projects. Another major constraint is that support and troubleshooting of the devices is difficult and often slow. If the device does not work, students, working at a distance, will often spend considerable time attempting to fix the problem before contacting tutors or other support personnel. These contacts are likely to be by telephone (or similar) or email. This is a rather different context from a traditional university, where students use physical computing devices in labs where skilled staff and technicians are available to step in and swiftly resolve trivial, but show-stopping, configuration or hardware errors. The device must be sufficiently reliable to have a very low manufacturing defect rate, survive postage to the student, and continue to function after over a year in the untidy environment of a family home, with all the attendant possible insults from pets and small children that are present in a home environment. Finally, the kit must be cheap enough that it need not be returned to the Open University on completion of a student's studies, as past experience has shown that the cost of receiving and refurbishing the kits is prohibitive. The SenseBoard has, as the name implies, a number of sensors mounted on the board: a slider, a non-latching push button, a microphone, an infra-red sensor that detects signals from remote controls, and four 3.5mm sockets for plugging in additional sensors. The SenseBoard kit comes with a tilt sensor, a temperature sensor, and a light sensor; students can easily make or connect additional ones, such as pressure sensors and rheostats. The board also has some outputs: mounted on the board is a bank of seven LEDs in various colours, and the kit also comes with an IR LED on a long lead and a stepper motor, both of which can be plugged in to the board. There is scope to connect ICICTE 2015 Proceedings 45 DC motors and servomotors, but we do not supply these in the kit. This range of sensors and outputs means the SenseBoard is a flexible physical computing device capable of many uses. Everything that plugs in to the SenseBoard does so with simple non-reversible sockets that do not require the insertion of leads into small holes in a breadboard or the manipulation of small individual components. The SenseBoard connects to a host computer via a USB cable. To keep things simple, the SenseBoard is not capable of autonomous operation and must be controlled by a host computer. Given the context of students starting computer science studies at a distance, the supplied programming environment also required that students be able to get started easily with the programming environment without being held back by trivial syntax errors common in novice programmers working with traditional textual programming languages for the first time. Therefore, we developed our own programming environment, Sense, based on the popular Scratch graphical, block-based, programming environment (Maloney, Resnick, Rusk, Silverman, & Eastmond, 2010). Sense was based on Scratch 1.3 and extended to make it suitable for undergraduate study. Main extensions were introduction of list variables, inclusion of blocks to control and read the SenseBoard and to read and write text files, and addition of blocks for network communication. Various other changes were made, including addition of more data manipulation blocks and numerous user interface changes. The network communication blocks allow Sense to read arbitrary content from the Web, but also have dedicated support for reading RSS feeds, exposing the content of the feed as a list-like structure. We also allow Sense to write data to a dedicated server run by the Open University; this data is made available as both a simple web page and as an RSS feed, suitable for reading by Sense. This feature allows for individual students to write data such as logs, for students to view each others' data for collaboration on group projects, and for near real-time communication between students, either for chat, distributed presence, or simple game controls. (The extension of lists was folded back into the main Scratch 1.4 and hence Scratch 2, developed by MIT.) While the most often used interface for the SenseBoard is Sense, we have also developed a Python library for driving the SenseBoard (Smith & Smith 2015).