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South African Computer Journal

On-line version ISSN 2313-7835
Print version ISSN 1015-7999

SACJ vol.32 n.2 Grahamstown Dec. 2020


  • Initiating the differentiated learning design in response to the changes in the learner profile (Section 4.2.2)

  • Evaluation of the impact of the differentiated learning design on learner satisfaction, learning effectiveness and efficiency closes the process model loop. This step is represented in the model as an off-page reference since this step is yet to be modelled as part of future work.



    Existing learning analytics process models suffer from a too narrow focus on the data collection and analysis steps of learning interventions. This myopic view on the technical aspects of learning analytics often results in interventions lacking pedagogical validation and ethical reflection. When the first step of the learning analytics process is data collection, there is likely to be a lack of clarity on the goal of the intervention. An ethical learning analytics code of practice requires participants to be explicitly made aware of the goal of the data collection, analysis and intervention. A learning analytics process model also needs to acknowledge the fact that more questions may arise after the initial analysis is done. There is, therefore, a need for a more comprehensive abstraction of the learning analytics process.

    Regarding Design Science Research, the knowledge contribution made in this paper is that of an emerging model that addresses limitations in existing learning analytics models. The proposed solution can be classified as an exaptation of a tried-and-tested model used in e-commerce and applying it to the online learning application domain.

    The process model proposed in this paper emerged by incorporating steps of an established web analytics process with educational theory, an ethical learning analytics code practice and a layered abstraction of online learning design. The pedagogical aspects of the model are derived from the concept of differentiated instruction, a teaching approach that prescribes modifying instruction based on the diverse needs of individuals sharing similar attributes. The online learning design is abstracted through several layers that systematically guides instructional designers through the process of designing and developing tailored learning objects to satisfy a range of diverse learner needs. The learner modelling phase prescribes a review of ethical requirements, drafting of an ethical code of practice and implementation of mechanisms to ensure principles of data privacy and equity are upheld throughout data collection, analysis and intervention. The learner modelling phase also provides an optional step to test new hypotheses should they arise after initial analysis.

    Many education institutions are adopting Learning Management Systems as the online learning environment. However, Learning Management Systems mostly suit a one-size-fits-all approach to teaching. Future work includes instantiating the learning design phase and learner modelling phase in a Learning Management System to determine whether it is possible to provide differentiated instruction and maintain a dynamic learner profile based on the data logged by the system. The ultimate goal for the proposed model is to enable the discovery of relevant learner cognitive and affective attributes that influence online learning behaviours. While the contribution of this paper is on how learning analytics can inform learning design, a model to measure the impact of the changes to the learning design also remain future work.



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    Received: 01 Mar 2017

    ^rND^sAtif^nY.^rND^sBailey^nM.^rND^sDittrich^nD.^rND^sKenneally^nE.^rND^sMaughan^nD.^rND^sBaker^nR. S.^rND^sYaceff^nK.^rND^sChatti^nM. A.^rND^sDyckhoff^nA. L.^rND^sSchroeder^nU.^rND^sThüs^nH.^rND^sCook^nD. A.^rND^sDesmarais^nM. C.^rND^sBaker^nR.^rND^sGregor^nS.^rND^sHevner^nA. R.^rND^sHevner^nA.^rND^sMarch^nS.^rND^sPark^nJ.^rND^sRam^nS.^rND^sHoel^nT.^rND^sChen^nW.^rND^sHundhausen^nC.^rND^sOlivares^nD.^rND^sCarter^nA.^rND^sKirschner^nP A.^rND^sKop^nR.^rND^sFournier^nH.^rND^sLuna^nJ. M.^rND^sCastro^nC.^rND^sRomero^nC.^rND^sManning^nS.^rND^sStanford^nB.^rND^sReeves^nS.^rND^sMelia^nM.^rND^sPahl^nC.^rND^sMerceron^nA.^rND^sBlikstein^nP^rND^sSiemens^nG.^rND^sRoberts^nL. D.^rND^sHowell^nJ. A.^rND^sSeaman^nK.^rND^sGibson^nD. C.^rND^sRomero^nC.^rND^sVentura^nS.^rND^sRomero^nC.^rND^sVentura^nS.^rND^sRomero^nC.^rND^sVentura^nS.^rND^sGarcía^nE.^rND^sSiemens^nG.^rND^sSimon^nH. A.^rND^sSlade^nS.^rND^sPrinsloo^nP.^rND^sSteiner^nC. M.^rND^sKickmeier-Rust^nM. D.^rND^sAlbert^nD.^rND^sTomlinson^nC. A.^rND^sBrighton^nC.^rND^sHertberg^nH.^rND^sCallahan^nC. M.^rND^sMoon^nT. R.^rND^sBrimijoin^nK.^rND^sReynolds^nT.^rND^sVerbert^nK.^rND^sDuval^nE.^rND^sKlerkx^nJ.^rND^sGovaerts^nS.^rND^sSantos^nJ. L.^rND^sViberg^nO.^rND^sHatakka^nM.^rND^sBälter^nO.^rND^sMavroudi^nA.^rND^sWillis^nJ. E.^rND^sSlade^nS.^rND^sPrinsloo^nP^rND^1A01^nTimothy Lee^sSon^rND^1A02^nJanet^sWesson^rND^1A03^nDieter^sVogts^rND^1A01^nTimothy Lee^sSon^rND^1A02^nJanet^sWesson^rND^1A03^nDieter^sVogts^rND^1A01^nTimothy Lee^sSon^rND^1A02^nJanet^sWesson^rND^1A03^nDieter^sVogts



    Designing a natural user interface to support information sharing among co-located mobile devices



    Timothy Lee SonI; Janet WessonII; Dieter VogtsIII

    IDepartment of Computing Sciences, Nelson Mandela University, South Africa.
    IIDepartment of Computing Sciences, Nelson Mandela University, South Africa.
    IIIDepartment of Computing Sciences, Nelson Mandela University, South Africa.




    Users of mobile devices share their information through various methods, which are supported by mobile devices. However, the information sharing process of these methods are typically redundant and sometimes tedious. This is because it may require the user to repeatedly perform a series of steps to share one or more selected files with another individual. The proliferation of mobile devices support new, more intuitive, and less complicated solutions to information sharing in the field of mobile computing. The aim of this paper is to present MotionShare, which is a NUI application that supports information sharing among co-located mobile devices. Unlike other existing systems, MotionShareÕs distinguishing attribute is its inability of relying on additional and assisting technologies in determining the positions of devices. A primary example is using an external camera to determine device positioning in a spatial environment. An analytical evaluation investigated the accuracy of device positioning and gesture recognition, where the results were positive. The empirical evaluation investigated any usability issues. The results of the empirical evaluation showed high levels of user satisfaction and that participants preferred touch gestures to point gestures.
    Human-centered computing ~ Interaction techniques Human-centered computing ~ Ubiquitous and mobile computing systems and tools

    Keywords: information sharing, natural user interfaces, mobile computing, gesture-based interaction




    Communication between users and their mobile devices continuously increases. As a result, the need for information sharing emerges. The advancements in mobile computing, specifically the computational and data storage capabilities, have attributed to the increased information sharing rate between users and their mobile devices. Existing information sharing methods on mobile devices are typically manual in nature. The process can be cumbersome and dependent on the quantity of information to be shared as well as the number of recipients. Therefore, these methods can become time-consuming and ineffective.

    Due to the increasing prevalence of Natural User Interfaces (NUIs), standard interaction methods with mobile device are also increasingly replaced with more intuitive interaction techniques. Intuitive is the easy understanding of a concept without any conscious reason (Britton, Setchi & Marsh, 2013). Human-computer interaction has evolved to the include various gestures (tap, point, swipe, and drag) that are prevalent in NUIs (Oh, Robinson & Lee, 2013). NUI interaction techniques are characterised as natural and intuitive to users. Therefore, task completion is less time-consuming and users are able to easily perform task actions (Oh et al., 2013). Increasingly, new application areas of NUIs show promise and further advance the current generation of interactive computing (Seow, Wixon, Morrison & Jacucci, 2010).

    Proxemics involves the study of sociological, behavioural, and cultural features between individuals and their devices (Dingler, Funk & Alt, 2015). Proxemics also refers to the movement, orientation, and distances between devices. In computer vision and robotics, an object's pose refers to both the object's position and orientation (Ilic, 2010). In this paper, pose means the device's location and orientation in relation to other devices in the environment.

    A co-located environment refers to a forum where users and their mobile devices are collectively gathered (formal or informal environment) (Heikkinen & Porras, 2013). As long as the users are in close proximity to each other, the environment can occur indoors or outdoors. User proximity is important as it allows NUI interaction techniques to support information sharing among co-located mobile devices. Therefore, co-located is the close proximity of users and their mobile devices to each other (indoors or outdoors), with limited movement of users (standing or seated near a table).

    The aim of this paper is to present an NUI application, called MotionShare, to support information sharing among co-located mobile devices. MotionShare calculates the poses of co-located mobile devices and applies this to NUI gestures to facilitate information sharing (documents, images, and media) among selected recipients. The research contribution to mobile computing is MotionShare's accuracy and usability that we have evaluated.

    This article extends our SAICSIT 2016 paper (Lee Son, Wesson & Vogts, 2016).



    This section discusses information sharing and NUIs. The related work presents and identifies the challenges of information sharing as well as the benefits and shortcomings of existing NUI systems.

    2.1 Information sharing

    Information sharing is any activity where information (natural, electronic, or other form) is transferred between individuals, organisations, or devices through by any means of transference (Mesmer-Magnus & DeChurch, 2009).

    Widespread communication and coordination of people has caused a variety of information methods to occur. These methods are either in a digital or natural form. The proliferation of computing technology has made digital information sharing methods possible. Although natural information sharing methods will always be prevalent, their digital counterparts have made life easier. For example, sending files (images, documents, music, or videos) can be done transferred between devices via flash drives, Dropbox, Bluetooth, or email attachments.

    Co-located information sharing only occurs when individuals are located within the same environment, such as a room (Singleton, 2014). Consequently, continuous information sharing is expected because individuals are face-to-face. Furthermore, individuals in a co-located environment may know each other or have a shared context, such as living in the same city, studying at the same university, or working for the same organisation (Kahai, 2008).

    Mobile devices support several technologies to share information, which include Near Field Communication (NFC), Dropbox, Bluetooth, WiFi, and Email. Exsting information sharing systems were identified that use one or more of these methods. These systems are Xender (Anmobi, Inc., 2015), ShareLink (ASUSTeK Computer Inc., 2015), Feem (FeePerfect, 2015), and SuperBeam (LiveQoS, 2015). Xender, Share Link, and Feem use WiFi to share information, whereas SuperBeam uses NFC, Wi-Fi Direct, QR Codes, or Manual Sharing Key. In all these systems, the selection of a files and recipient is performed by single touch. Typically, when selecting a recipient, an icon or text list of device names is displayed. However, SuperBeam also supports QR Codes or NFC to select a recipients. None of the systems identified sufficiently determine the device pose for NUI interaction techniques to be used. In these systems, file sharing among multiple devices also requires the above process to be repeatedly performed.

    2.2 Natural User Interfaces

    NUIs typically allow individuals to perform natural movements to manipulate on-screen content or control of an application (Yao, Fernando & Wang, 2012). Various interaction techniques (multi-touch, gesture recognition, speech, eye tracking, and proxemics) allow user interaction in NUIs. NUIs provide a new perspective on user interaction with content displayed on devices. There are several NUI definitions, all of which originated from Blake's (2013) definition:

    A natural user interface is a user interface designed to reuse existing skills for interacting appropriately with content.

    This definition illustrates three crucial aspects about NUIs, namely: NUIs are designed, NUIs reuse existing skills, and NUIs provide appropriate interaction with content.

    Natural Interaction is a user experience objective that is extensively researched in most interaction fields and not limited to NUIs (Tavares, Medeiros, de Castro & dos Anjos, 2013).

    Consequently, it can be presented that natural interaction is the effect of transparent interfaces, which are based on previous knowledge, and where the users feel like they are interacting directly with the content. (Wendt, 2013)

    The first NUI objective is derived from the natural interaction definition, whereby content and context familiarity ensures users can understand the interaction. Typically, NUIs should prioritise the content and its supporting technologies be ubiquitous (Blake, 2013). The second NUI objective is content-centric, whereby the NUI should facilitate content accessibility. NUI literature (Blake, 2013; Valli, 2008; Wigdor & Wixon, 2011) perpetuates that the appropriate selection of a technique is dependent on the proper understanding of the context. Thus, the third NUI objective is the importance of context, where understanding the context ensures the appropriate interaction techniques are selected and used. The cognitive load is reduced by reusing skills that users already possess, thus freeing up the mental capacity to understand and reuse different interactions with an NUI. The fourth NUI objective is reducing the user cognitive load of NUI interaction.

    Gestures are any motion that involves physical movements of an individual's body, for example, hands, fingers, head, or feet (Billinghurst, Piumsomboon & Bai, 2014). Gestures provide a natural, direct, and intuitive way of interacting with a computing device, allowing easier interaction for all types of users, including the elderly (Hollinworth & Hwang, 2011). There are two types of gestures, namely touch and in-air gestures (X. A. Chen, Schwarz, Harrison, Mankoff & Hudson, 2014). Touch gestures are predominant in touch screen interfaces where the user performs a predefined gesture to achieve a specific system response. In-air gestures are any movements of the user's body that are recognised by the system without touching the screen (Agrawal et al., 2011). A benefit of in-air gestures is their natural feel as they naturally accompany speech interaction. However, in-air gestures are susceptible to several limitations, such as context dependence, user fatigue, social acceptability, and gesture recognition (Agrawal et al., 2011; Bratitsis & Kandroudi, 2014).

    Several NUI systems are available, which use different interaction techniques depending on the context. Different NUI interaction techniques are used to support the specific context of each system. These systems were selected for review on the basis that they required no additional hardware. The systems reviewed were Zapya (DewMobile, 2016), AirLink (K.-Y. Chen, Ashbrook, Goel, Lee & Patel, 2014), MobiSurf (Seifert et al., 2012), Gesture On (Lu & Li, 2015), and Flick (Ydangle Apps, 2013). MobiSurf, Flick, and Zapya only support single file sharing. All of these systems use touch gestures, except AirLink, which uses in-air gestures and code words to share information. None of these systems use pose information to determine the location and orientation of the mobile devices in the physical environment. Table 1 summarises the applications with their respective communication technologies used and the types of information sharing supported.



    This research selected and followed the Design Science Research (DSR) methodology. It is an appropriate methodology because it requires the development of a proxemic prototype NUI to support information sharing among co-located mobile devices. The development of this prototype directly correlates with the development of an artefact to solve the identified problem, which is one of the core activities in the DSR methodology, namely Design and Develop Artefact (Peffers, Tuunanen, Rothenberger & Chatterjee, 2007).

    3.1 Application of DSR

    The DSR methodology can be used as a framework for conducting research based on Design Science, which involves the performance of the following activities:

    Identify problem and motivate: defining specific research problem and justification of a solution;

    Define objectives of a solution: inferring the solution objectives derived from the problem definition and knowledge;

    Design and development: involves creating the artefact solution;

    Demonstration: demonstrating the artefact's efficacy to solve the defined problem;

    Evaluation: observing and measuring if and/or how well the artefact supports a solution to the defined problem, by comparing the solution objectives to actual observed results from the artefact in the demonstration phase; and

    Communication: communicating the importance of the problem, the artefact, its utility and novelty, rigor of its design, and its effectiveness to relevant audiences.

    During this research, several research strategies were employed, namely: literature study, focus groups, prototyping, and experiments. The various DSR methodology activities and cycles (Relevance, Design, and Rigor) incorporated these strategies.

    3.2 Research questions

    This research was guided through addressing the primary research question:

    How can a proxemic Natural User Interface be designed to provide an accurate and usable solution to support information sharing among co-located mobile devices?

    The following secondary questions were formulated to answer the primary research question:

    RQ1. What are the shortcomings of existing information sharing methods currently used by mobile devices?

    RQ2. What are the benefits and shortcomings of existing NUI interaction techniques for information sharing?

    RQ3. How should the relative pose for colocated mobile devices be calculated?

    RQ4. How should NUI interaction techniques be designed to support information sharing among co-located mobile devices?

    RQ5. How can a proxemic prototype NUI be developed to support information sharing among co-located mobile devices?

    RQ6. How accurate and usable is the proxemic prototype NUI in supporting information sharing among co-located mobile devices?

    Each of these research questions was addressed by applying at least one or more research strategies (Section 3.1) in one of the DSR methodology activities.



    This section discusses how MotionShare determines the positions of the co-located mobile devices in an environment. The pose information was used to design the information sharing process. Focus groups were conducted to determine the most suitable NUI gestures to be implemented in MotionShare, which were used to share information among co-located mobile devices.

    4.1 Positioning techniques

    For NUI interaction techniques to be used in information sharing, the pose of each device in relation to one another is required. Existing information sharing technologies (Section 2.1) only provide coarse-grained granularity. For this research, a more fine-grained approach is required for information sharing in a co-located environment. The distance between devices and the orientation of the devices is required for the computation of the poses of every device in the environment. There are other existing indoor positioning solutions (Estimote and iBeacon) that are small computer beacons used for indoor positioning. These solutions use Bluetooth 4.0 Smart, also known as Bluetooth Low Energy (BLE), an accelerometer, and a temperature sensor (Estimote, Inc., 2018; Apple, Inc., 2018). However, these solutions use hardware additional to the user's mobile device. Thus, the challenge was to solve this particular problem without the use of additional hardware.

    4.1.1 Determining distance

    Smartphones can use different techniques to determine their own distance. Table 2 presents several criteria to measure the different distance techniques (Hightower & Borriello, 2001).

    These techniques are global positioning system (GPS), cell tower triangulation and Wi-Fi positioning. GPS is highly accurate (95.00%) and distance is within metres, but requires high level of energy consumption and is unable to perform within indoor environments. Although cell tower triangulation is energy efficient and available almost everywhere in the world, it still lacks the accuracy and precision provided by GPS (accuracy within metres). Wi-Fi positioning is best suited for an indoor environment; however, it requires multiple access points (AP) to be located nearby to function properly, and is only able to provide a distance to within an accuracy of 60 metres. Table 3 compares the distance techniques using the criteria identified in Table 2.

    All the discussed techniques provided coarse-grained granularity, and ideally for this research, a more fine-grained technique is required. Therefore, experiments were conducted to determine if a more fine-grained solution was available. Several experiments were conducted using Bluetooth. The objective of these experiments was to determine if this technology could be used to determine the distance between two mobile devices. Each experiment involved two smartphones that was placed at different distance increments. The distance increments commenced at 25cm and went up to 200cm. These increments were considered to be a fair because it represents the expected distance that users would either be seated or standing apart in a co-located environment.

    A prototype using the Bluetooth Received Signal Strength Indicator (RSSI) was developed, installed, and used on each device to determine the RSSI values. The RSSI values displayed on each device were recorded by observation and data logging. Every experiment conducted was repeated several times to ensure an adequate data sample was obtained to develop a more accurate model for the selected machine learning (ML) algorithms.

    The collected data from the Bluetooth experiments was subjected to various ML algorithms. Table 4 shows the performance of these algorithms in classifying the distance based on the Bluetooth RSSI values. The IB1 classifier had the highest accuracy in correctly classifying the instances. Therefore, the IB1 classifier was selected and used within MotionShare.



    The IB1 algorithm is an instance-based nearest neighbour classifier (Devasena, 2013). It uses normalised Euclidean distance to determine the training instance closest to the given test instance, and predicts the same class as this training instance. 10-fold cross-validation was used with IB1. This meant that the dataset was split into 10 equal parts (folds). Using the 10-fold cross-validation also meant that 90% of the dataset was used for the training (and 10% for testing) in each fold test.

    Confusion matrices are used in ML to visualise the performance of a specific algorithm (Markham, 2014). Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. The value at each intersection between a column and row represents the number of predictions classified. The ideal scenario is to have the value only appear in the "diagonal".

    Table 5 represents the confusion matrix for the IB1 algorithm. The IB1 algorithm correctly classified 50cm (92%) and misclassified it as 25cm (4%), 75cm (2%), and 100cm (2%). Overall, the IB1 algorithm correctly classified instances with an accuracy of 81.25%, which was deemed to be an acceptable rate.



    All the results from the experiments were aggregated into a single visualisation to illustrate the relationship between Bluetooth RSSI and distance. The experiments showed that an inverse relationship exists between these two variables (Figure 1).



    Bluetooth RSSI values are, however, sensitive to some variables in the environment, so a test: environment was chosen that had low environmental interference.

    4.1.2 Determining Orientation

    The design of a digital compass was required to determine the orientation of the mobile devices1 Consequently, the position and motion sensors embedded in mobile devices were utilised. These sensors were the accelerometer, magnetometer, and gyroscope.

    An accelero meter is an embedded sensor in a mobile device used to me asure the acceleration forces on all three physical axes (x, y, and z) and can determine the device's physical position (Aviv, Sapp, Blaze & Smith, 2012). A magnetometer is a magnetic sensor embedded in a mobile device to determine the heading ef the device, erovided she user is holding it parallel to the ground (Zhang & Sawchuk, 2012).

    Similarly, a gyroscope is an embedded sensor that provides an additional dimension to the information supplied by the accelerometer by measuring the rotation or twist of the Oevice (Thomason & Wang, 2012). The gyroscope measures the angular rotational velocity od a device. Unlike the aceel0rometer, the gyroscope is not affected by gravity. Thus, the accelerometer and gyroscope measure the rate od change differently. In practice, this means that an accelerometer will measure the directional movement of a device, but will not be able to accurately resolve its lateral orientation or tilt during this movement accurately, without the use of the gyroscope which would provide the fdditional information (Kratz, Rohs & Essl, 2013).

    These sensors outp°t sensor data that varies at a high rate, similarly to the Bluetooth RSSI values. The solution was to poll the sensor data at an interval of two seconds and only extract those values that were useful to this contex and eliminate unnecessary noise. Applying a low-pass filter resolved the issue of the sensor data varying considerably.

    A low-pass filter is a smoothing algorithm that smooths the sensor values by filtering out high-frequency noise and "passes" low-frequency or slowly varying changes (Lee, 2014). Consequently, a more stable compass is displayed and the orientation changes are smoother. The accuracy of the compass was compared to existing compass applications on the Google Play Store and it was found to be similar (informal testing).

    Figure 2 illustrates how multiple sensors were combined to create sensor fusion. The sensor data from the accelerometer, magnetometer, and gyroscope were combined through the low-pass filter to provide an improved compass. The distance and orientation information was combined to determine the pose of the different devices in relation to each other.



    4.1.3 MotionShare architecture

    MotionShare was designed as an Android application to support information sharing amono co-located devices and, therefore, a client-server architecture was required (Baotic, 2014). There are two roles in MotionShare, namely server and client. Any device can act as a server and the other devic°s serve as clients. The server facilitates the communication of the relative positions (poses) of the devices between the clients and itself using Wi-Fi. The server's purpose is to create a private and secure Wi-Fi hotspot, wh°reby clients can join the password-protected network. The server can determine the network service set identifier (SSID) and the password. The client requires the input of the network SSID and the encrypted password. All of the devices are able to send and receive information. Figure 3 illustrates the poses of several devices that are randomly placed on a table.


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