User Study Research Articles

Towards Reducing Continuous Emotion Annotation Effort During Video Consumption: A Physiological Response Profiling Approach

Emotion-aware video applications (e.g., gaming, online meetings, online tutoring) strive to moderate the content presentations for a more engaging and improved user experience. These services typically deploy a machine-learning model that continuously infers the user's emotion (based on different physiological signals, facial expressions, etc.) to adapt the content delivery. Therefore, to train such models, the emotion ground truth labels also need to be collected continuously. Typically, those are collated as emotion self-reports from users in a continuous manner (using an auxiliary device such as a joystick) when they watch some videos. This process of continuous emotion annotation not only increases the cognitive load and survey fatigue but also significantly deteriorates the viewing experience. To address this problem, we propose a framework, PResUP that probes a user for emotion self-reports opportunistically based on the physiological response variations of the user. Specifically, the framework implements a sequence of phases - (a) user profile construction based on physiological responses, (b) similar user clustering based on the user profile, and (c) training a parameterized activation-guided LSTM (Long short-term memory) model by sharing data among similar users to detect the opportune self-report collection moments. All these steps together help to reduce the continuous emotion annotation overhead by probing at the opportune moments without compromising the annotation quality. We evaluated PResUP by conducting a user study (N=36) during which the participants watched eight videos, and their physiological responses and continuous emotion self-reports were recorded. The key results from this evaluation reveal that PResUP reduces the annotation overhead by reducing the probing rate by an average of 34.80% and detects the opportune probing moments with an average TPR of 80.07% without compromising the annotation quality. Motivated by these findings, we deployed PResUP by performing a follow-up user study (N=18). In this deployment scenario, we also obtained similar performance in terms of the probing rate reduction (average reduction of 38.05%), and opportune moment detection performance (average TPR of 82.26%). These findings underscore the utility of PResUP in reducing the continuous emotion annotation effort during video consumption.

Exploration of Eye-Tracking Methodologies in Usability Testing of Digital Health Technology: A Rapid Review.

Eye-tracking is deemed a promising methodology for usability evaluation studies in healthcare, however clear theoretical guidance and practice remains lacking. A rapid review was performed on current use of eye tracking as a usability evaluation method on digital health technologies in the period of 2019 to 2024. Usability evaluation studies were included when they described a digital health technology intervention in which eye-tracking technologies were applied. To gain insight into how eye-tracking technologies contributed to measuring digital health technologies' usability, data was extracted on the use of eye-tracking for usability and key study findings. Seventeen papers were included in the review. Findings show that eye-tracking is frequently combined with other usability evaluation methods, with high methodological diversity, to test the usability of DHT. Future research is needed to enhance understanding of the effectiveness of eye-tracking outcomes in DHT usability testing when combined with other usability evaluation methods in order to provide (usability) researchers theoretical guidance on its application.

SmartDampener: An Open Source Platform for Sport Analytics in Tennis

In this paper, we introduce SmartDampener, an open-source tennis analytics platform that redefines the traditional understanding of vibration dampeners. Traditional vibration dampeners favored by both amateur and professional tennis players are utilized primarily to diminish vibration transmission and enhance racket stability. However, our platform uniquely merges wireless sensing technologies into a device that resembles a conventional vibration dampener, thereby offering a range of tennis performance metrics including ball speed, impact location, and stroke type. The design of SmartDampener adheres to the familiar form of this accessory, ensuring that (i) it is readily accepted by users and robust under real-play conditions such as ball-hitting, (ii) it has minimal impact on player performance, (iii) it is capable of providing a wide range of analytical insights, and (iv) it is extensible to other sports. Existing computer vision systems for tennis sensing such as Hawk-eye and PlaySight, rely on hardware that costs millions of US dollars to deploy with complex setup procedures and is susceptible to lighting environment. Wearable devices and other tennis sensing accessories, such as Zepp Tennis sensor and TennisEye, using intrusive mounting locations, hinder user experience and impede player performance. In contrast, SmartDampener, a low-cost and compact tennis sensing device, notable for its socially accepted, lightweight and scalable design, seamlessly melds into the form of a vibration dampener. SmartDampener exploits opportunities in SoC and form factor design of conventional dampeners to integrate the sensing units and micro-controllers on a two-layer flexible PCB, that is bent and enclosed inside a dampener case made of 3D printing TPU material, while maintaining the vibration dampening feature and further being enhanced by its extended battery life and the inclusion of wireless communication features. The overall cost is $9.42, with a dimension of 21.4 mm × 27.5 mm × 9.7 mm (W × L × H) and a weight of 6.1 g and 5.8 hours of battery life. In proof of SmartDampener's performance in tennis analytics, we present various tennis analytic applications that exploit the capability of SmartDampener in capturing the correlations across string vibration, and racket motion, including the estimation of ball speed with a median error of 3.59 mph, estimation of ball impact location with accuracy of 3.03 cm, and classification of six tennis strokes with accuracy of 96.75%. Finally, extensive usability studies with 15 tennis players indicate high levels of social acceptance of form factor design for the SmartDampener dampener in comparison with alternative form factors, as well as its capability of sensing and analyzing tennis stroke in an accurate and robust manner. We believe this platform will enable exciting applications in other sports like badminton, fitness tracking, and injury prevention.

Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment

μEMA is a data collection method that prompts research participants with quick, answer-at-a-glance, single-multiple-choice self-report behavioral questions, thus enabling high-temporal-density self-report of up to four times per hour when implemented on a smartwatch. However, due to the small watch screen, μEMA is better used to select among 2 to 5 multiple-choice answers versus allowing the collection of open-ended responses. We introduce an alternative and novel form of micro-interaction self-report using speech input - audio-μEMA- where a short beep or vibration cues participants to verbally report their behavioral states, allowing for open-ended, temporally dense self-reports. We conducted a one-hour usability study followed by a within-subject, 6-day to 21-day free-living feasibility study in which participants self-reported their physical activities and postures once every 2 to 5 minutes. We qualitatively explored the usability of the system and identified factors impacting the response rates of this data collection method. Despite being interrupted 12 to 20 times per hour, participants in the free-living study were highly engaged with the system, with an average response rate of 67.7% for audio-μEMA for up to 14 days. We discuss the factors that impacted feasibility; some implementation, methodological, and participant challenges we observed; and important considerations relevant to deploying audio-μEMA in real-time activity recognition systems.

Pre-Pilot Usability Study of the Take-A-Breath Project: A Personalized Platform for Respiratory Disease Management.

This study aims to evaluate the patient's platform of the Take-A-Breath project-a Greek project for personalized respiratory disease support. The pre-pilot usability study, involving 11 participants, employed a mixed-methods approach. While calculated scores indicate overall acceptance, concerns are identified, particularly regarding the learning curve needed for the guided inhalation feature, the application's core functionality. Users appreciate the feature's utility and design after repeated use. Findings recommend user manuals and healthcare professional training, providing essential insights for the upcoming RCT.

Physicians' Views of a Digital Intervention to Support Respiratory Patients: Take-a-Breath Pre-Pilot Usability Study.

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end's usability. This study aims to evaluate the Healthcare Professional's (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.

Usability Checklists for Health Technology: Case Study and Experts' Opinions.

Application of usability evaluations throughout the health technology lifecycle is necessary to improve the efficiency, safety, and effectiveness of health service delivery. Unfortunately, technology vendors and healthcare organizations may not have funding, time or expertise to conduct usability studies. In this paper, we describe how usability checklists can potentially fill this gap. First, we introduce a case study using a checklist to identify usability issues with a primary care dashboard. Then we provide an expert summary of the strengths and limitations of usability checklists. Findings suggest that checklists are efficient to identify important usability issues. They can be used effectively by project team members - including clinicians - without formal usability training. However, checklists should complement rather than replace usability evaluations with representative users.

ChatIoT: Zero-code Generation of Trigger-action Based IoT Programs

Trigger-Action Program (TAP) is a simple but powerful format to realize intelligent IoT applications, especially in home automation scenarios. Existing trace-driven approaches and in-situ programming approaches depend on either customized interaction commands or well-labeled datasets, resulting in limited applicable scenarios. In this paper, we propose ChatIoT, a zero-code TAP generation system based on large language models (LLMs). With a novel context-aware compressive prompting scheme, ChatIoT is able to automatically generate TAPs from user requests in a token-efficient manner and deploy them to the TAP runtime. Further, for those TAP requests including unknown sensing abilities, ChatIoT can also generate new AI models with knowledge distillation by multimodal LLMs, with a novel model customization method based on deep reinforcement learning. We implemented ChatIoT and evaluated its performance extensively. Results show that ChatIoT can reduce token consumption by 26.1-84.9% and improve TAP generation accuracy by 4.2-65.5% compared to state-of-the-art approaches in multiple settings. We also conducted a real user study, and ChatIoT can achieve 91.57% TAP generation accuracy.

SilverCycling: Exploring the Impact of Bike-Based Locomotion on Spatial Orientation for Older Adults in VR

Spatial orientation is essential for people to effectively navigate and interact with the environment in everyday life. With age-related cognitive decline, providing VR locomotion techniques with better spatial orientation performance for older adults becomes important. Such advancements not only make VR more accessible to older adults but also enable them to reap the potential health benefits of VR technology. Natural motion-based locomotion has been shown its effective in enhancing younger users' performance in VR navigation tasks that require spatial orientation. However, there is a lack of understanding regarding the impact of natural motion-based locomotion on spatial orientation for older adults in VR. To address this gap, we selected the SilverCycling system, a VR bike-based locomotion technique that we developed, as a representative of natural motion-based locomotion, guided by findings from our pilot study. We conducted a user study with 16 older adults to compare SilverCycling with the joystick-based controller. The findings suggest SilverCycling potential to significantly enhance spatial orientation in the open-road urban environment for older adults, offering a better user experience. Based on our findings, we identify key factors influencing spatial orientation and propose design recommendations to make VR locomotion more accessible and user-friendly for older adults.

Object manipulation based on the head manipulation space in VR

Object manipulation is fundamental in virtual and augmented reality, where efficiency and accuracy are crucial. However, repetitive object manipulation tasks using the hands can lead to arm fatigue, and in some scenarios, hands may not be feasible for object manipulation. In this paper, we propose a novel approach for object manipulation based on head movement. Firstly, we introduce the concept of head manipulation space and conduct an experiment to collect head manipulation space data to determine the manipulable space. Then, we propose a new method for object manipulation based on head speed and inter-frame viewpoint quality to enhance the efficiency and accuracy of head manipulation. Finally, we design two user studies to evaluate the performance of our head-based object manipulation method. The results show that our method is feasible in terms of task completion efficiency and accuracy compared to state-of-the-art methods and greatly reduces user fatigue and motion sickness. Moreover, our method significantly improves usability and reduces task load. Our method lays a foundation for head-based object manipulation in virtual and augmented reality and provides a new manipulation method for scenarios where hands are not suitable for object manipulation.

CoplayingVR: Understanding User Experience in Shared Control in Virtual Reality

Shared control of avatars in virtual reality is emerging as a promising method to enhance collaborative interactions. While the literature investigates shared control as an input modality to understand the design of functional experiences, the user experience associated with shared control remains underexplored. In this paper, we investigate the user experience of shared control using a networked VR environment, "CoplayingVR". Through a user study comprised of three gaming tasks in VR with 48 participants, we collect both quantitative and qualitative data on the user experience and performance of shared control. Our findings reveal that the shared control mode significantly improved user experience factors and brought novelty to the play. Furthermore, shared control significantly enhanced the task efficiency for novice users. Finally, we discuss how our findings inform the implications for research and practice of shared control for the growing body of research in VR.

EyeGesener: Eye Gesture Listener for Smart Glasses Interaction Using Acoustic Sensing

The smart glasses market has witnessed significant growth in recent years. The interaction of commercial smart glasses mostly relies on the hand, which is unsuitable for scenarios where both hands are occupied. In this paper, we propose EyeGesener, an eye gesture listener for smart glasses interaction using acoustic sensing. To mitigate the Midas touch problem, we meticulously design eye gestures for interaction as two intentional consecutive saccades in a specific direction without visual dwell. The proposed system is a glass-mounted acoustic sensing system with two pairs of commercial speakers and microphones to sense eye gestures. To capture the subtle movements of the eyelid and surrounding skin induced by eye gestures, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel impulse response (CIR) estimation schema that allows two speakers to transmit at the same time and in the same frequency band without collision. We implement eye gesture filtering and adversarial-based eye gesture recognition to identify eye gestures for interaction, filtering out daily eye movements. To address the differences in eye size and facial structure among different users, we employ adversarial training to achieve user-independent eye gesture recognition. We evaluate the performance of our system through experiments on data collected from 16 subjects. The experimental result shows that our system can recognize eight eye gestures with an average F1-score of 0.93, and the false alarm rate of our system is 0.03. We develop an interactive real-time audio-video player based on EyeGesener and then conduct a user study. The result demonstrates the high usability of the proposed system.

Learning beyond books: A hybrid model to learn real‐world problems

AbstractThere are several initiatives underway to improve the learning of software developers. These attempts include the integration of GitHub into software engineering classes, the creation of learning management systems, gamification approaches, and collaborative learning platforms. These initiatives have demonstrated promise in boosting students' collaborative growth and cooperation abilities, emphasizing their potential influence on improving learning experiences in practical areas. Books, on the other hand, remain basic in education, but their physical size limits their ability to explore all practical elements of a topic in depth. This limitation requires more research and application of theoretical information in real‐world circumstances. In this work, we address the issue of limited space in traditional books that frequently prevents complete presentation of practical elements of a topic. To address this issue, we propose an application that improves the reading experience and accelerates the learning process. To anticipate themes, we use a combination of latent Dirichlet allocation (LDA) algorithms and a generative pre‐trained transformer. First, utilizing LDA to find potential topic keywords inside the text and then leveraging generative pretrained transformer to predict topic names based on the LDA produced keywords. In addition, a query builder module produces and executes queries depending on the current page's topic, obtaining real‐world issues from Stack Overflow. The system classifies results by query‐title similarity, question‐answer ranking, and content quality before displaying them to users. This bridges the gap between theoretical knowledge and practical application. We illustrate the usefulness of suggested tool using simulations, comparison with existing tools and user studies. The majority of users provide favorable comments and find it interesting and helpful for improving the learning process.

Sense of Embodiment Supports Motor Learning and Adaptation in Tele-robotics

In this paper, we transition from the theoretical and experimental groundwork of manipulating and measuring Sense of Embodiment (SoE) to addressing a fundamental question: What is the purpose of optimizing the SoE in a teleoperation system? This exploration centers on investigating the potential positive effects of SoE on motor adaptation, the acceleration of motor learning, and the potential enhancement of task performance. The paper delves into this investigation by focusing on two critical research questions: 1) what is the effect of SoE on task performance in a perceptual-motor task? 2) What is the effect of SoE on the asymptote of the learning curve in a perceptual-motor task? Drawing insights from the existing literature, the hypothesis emerges that enhancing SoE yields positive effects, not only on task performance (H1) but also on the overall embodiment experience (H2). An additional layer of exploration is introduced through an exploratory research question: Are these results consistent across diverse scenarios and tasks? The study design encompasses two distinct user studies, each set in different applications and featuring various avatars, yet all anchored in similar tasks, specifically a modified peg-in-hole task: 1) in the first experiment, participants operated a robotic arm with a human-like hand as end-effector, and they were required to perform a classic peg-in-hole task; 2) in user study 2, the task is transformed into a variation that we called “peg-on-button”, wherein participants use a robotic arm with a gripper as the end-effector to press a lit button. In both studies, a consistent pattern emerges: a setup that fosters embodiment has a positive impact on motor learning and adaptation, resulting in improved task performance. A supportive setup also reduces the perception of the surrogate as a mere mediator between the operator and the remote environment, especially when contrasted with a setup that suppresses embodiment. The positive effects on motor learning and task performance advocate for the incorporation of embodiment-supportive designs in teleoperational setups. However, the nuanced relationship between SoE and long-term task performance prompts a call for further exploration and consideration of various factors influencing teleoperation outcomes across diverse scenarios and tasks.

VAEnvGen: A Real-Time Virtual Agent Environment Generation System Based on Large Language Models

Environment plays an important role in non-verbal communication for human-virtual agent interaction. Existing research explores the influence of an agent’s appearance and attributes to enhance human-virtual agent communication. However, there is no common practice for dynamically adjusting the surrounding environments of the virtual agent. In this paper, we introduce a real-time virtual agent environment generation system (VAEnvGen), which contributes to the field by enhancing users’ content perception and improving task performance through dynamic environment adjustment. The system dynamically analyzes both the appropriate communication environment and filters the key information according to the current context. Leveraging Large Language Models, it generates a pseudo-3D background space to create an engaging atmosphere and a dynamic foreground content space for vivid key information display, thereby significantly enhancing content perception. For widespread adoption and flexibility, VAEnvGen is developed as a web application. We further evaluate the impact of VAEnvGen on content perception, user attention, and subjective satisfaction through a mixed-design user study with 50 participants. Quantitative and qualitative results reveal significant improvements in content perception, task completion time, and user satisfaction when using VAEnvGen. The system effectively redistributes user attention from subtitles and the virtual agent itself to the dynamically generated background and key foreground information, leading to a more immersive and less fatiguing user experience.

Cue-based two factor authentication

With the increasing usage of cameras, the threat from video attacks has greatly increased in recent years in addition to shoulder surfing. Many organizations have implemented two-factor authentication to enhance security. However, attackers can still steal users' usernames and passwords from two-factor authentication through video attack or shoulder surfing and applied the credential stuffing attack, as most people use the same passwords on different applications. Cue-based authentication provides high protection against shoulder surfing attacks, but it remains vulnerable to video attacks. To mitigate the threats of video attacks, we propose cue-based two-factor authentication (i.e., Cue-2FA), which is distinct from other methods by separating cue display from response input (refer to Chapter 1). We conducted two user studies to compare the usability and security between Cue-2FA and a standard Time-based-One-Time-Password two-factor authentication (i.e., TOTP-2FA). The evaluate results revealed Cue-2FA provides both higher usability and stronger resistance to the shoulder surfing attack. However, when both the cue and response are recorded, Cue-2FA is not more resistant to the video attack than TOTP-2FA. To address this issue, we introduced misleading operations to Cue-2FA when inputting a response, which significantly improves the resistance to the video attack.

Implementation and Evaluation of a Gait Training Assistant for the Use of Crutches: Usability Study.

Surgical procedures on the lower extremities often require weight-bearing on crutches as part of the rehabilitation process. Orthopedic elective procedures enable patients to learn the correct use of crutches in a controlled preoperative setting. Digital assistance systems can safely circumvent a shortage of skilled staff and any contact restrictions that may be necessary. The usability of a newly developed gait training assistant (GTA) for the use of crutches will be evaluated. An intervention group trained to use crutches by the digital trainer will be compared with a control group trained to use crutches conventionally by a physiotherapist. As part of the development and implementation of a novel GTA, 14 patients learned to walk with crutches by completing specific exercises while receiving live feedback. Their movements were detected by a depth sensor and evaluated in real time. Specific parameters (step length, synchronous movement, crutch angle, and crutch distance to the feet) were compared with a control group (n=14) trained to use crutches by physiotherapists. The intervention group was also assessed by a physiotherapist. At the end of the study, the patients completed questionnaires to evaluate the usability of the system (Brooke's System Usability Scale score) and patient satisfaction. All patients trained by the novel GTA were able to use crutches correctly. The intervention group showed significantly better values for crutch angle (mean -6.3°, SD 3.5° vs mean -12.4°, SD 4.5°; P<.001) and crutch position (mean 3.3, SD 5.1 cm vs mean -8.5, SD 4.9 cm; P=.02). Both groups reported that they felt confident in the use of crutches, were able to follow the instructions, and enjoyed the training. Even though the majority (12/14, 86%) preferred physical therapy over a purely digital approach, most participants enjoyed using the system (13/14, 93%) and were interested in trying out other digital assistants (11/14, 79%). The usability of the GTA was rated above average by the majority (9/14, 64%) of the patients. The newly designed GTA is a safe method of teaching the use of crutches and is statistically superior to training by a physiotherapist. Even if patients prefer interaction with a physiotherapist over a purely digital approach, digital devices provide a safe and motivating opportunity to learn the essential locomotor skills for rehabilitation.

A High‐Scalability Graph Modification System for Large‐Scale Networks

AbstractModifying network results is the most intuitive way to inject domain knowledge into network detection algorithms to improve their performance. While advances in computation scalability have made detecting large‐scale networks possible, the human ability to modify such networks has not scaled accordingly, resulting in a huge ‘interaction gap’. Most existing works only support navigating and modifying edges one by one in a graph visualization, which causes a significant interaction burden when faced with large‐scale networks. In this work, we propose a novel graph pattern mining algorithm based on the minimum description length (MDL) principle to partition and summarize multi‐feature and isomorphic sub‐graph matches. The mined sub‐graph patterns can be utilized as mediums for modifying large‐scale networks. Combining two traditional approaches, we introduce a new coarse‐middle‐fine graph modification paradigm (i.e. query graph‐based modification sub‐graph pattern‐based modification raw edge‐based modification). We further present a graph modification system that supports the graph modification paradigm for improving the scalability of modifying detected large‐scale networks. We evaluate the performance of our graph pattern mining algorithm through an experimental study, demonstrate the usefulness of our system through a case study, and illustrate the efficiency of our graph modification paradigm through a user study.

A Handheld Tool for the Rapid Morphological Identification of Mosquito Species (VectorCam) for Community-Based Malaria Vector Surveillance: Summative Usability Study.

Malaria impacts nearly 250 million individuals annually. Specifically, Uganda has one of the highest burdens, with 13 million cases and nearly 20,000 deaths. Controlling the spread of malaria relies on vector surveillance, a system where collected mosquitos are analyzed for vector species' density in rural areas to plan interventions accordingly. However, this relies on trained entomologists known as vector control officers (VCOs) who identify species via microscopy. The global shortage of entomologists and this time-intensive process cause significant reporting delays. VectorCam is a low-cost artificial intelligence-based tool that identifies a mosquito's species, sex, and abdomen status with a picture and sends these results electronically from surveillance sites to decision makers, thereby deskilling the process to village health teams (VHTs). This study evaluates the usability of the VectorCam system among VHTs by assessing its efficiency, effectiveness, and satisfaction. The VectorCam system has imaging hardware and a phone app designed to identify mosquito species. Two users are needed: (1) an imager to capture images of mosquitos using the app and (2) a loader to load and unload mosquitos from the hardware. Critical success tasks for both roles were identified, which VCOs used to train and certify VHTs. In the first testing phase (phase 1), a VCO and a VHT were paired to assume the role of an imager or a loader. Afterward, they swapped. In phase 2, two VHTs were paired, mimicking real use. The time taken to image each mosquito, critical errors, and System Usability Scale (SUS) scores were recorded for each participant. Overall, 14 male and 6 female VHT members aged 20 to 70 years were recruited, of which 12 (60%) participants had smartphone use experience. The average throughput values for phases 1 and 2 for the imager were 70 (SD 30.3) seconds and 56.1 (SD 22.9) seconds per mosquito, respectively, indicating a decrease in the length of time for imaging a tray of mosquitos. The loader's average throughput values for phases 1 and 2 were 50.0 and 55.7 seconds per mosquito, respectively, indicating a slight increase in time. In terms of effectiveness, the imager had 8% (6/80) critical errors and the loader had 13% (10/80) critical errors in phase 1. In phase 2, the imager (for VHT pairs) had 14% (11/80) critical errors and the loader (for VHT pairs) had 12% (19/160) critical errors. The average SUS score of the system was 70.25, indicating positive usability. A Kruskal-Wallis analysis demonstrated no significant difference in SUS (H value) scores between genders or users with and without smartphone use experience. VectorCam is a usable system for deskilling the in-field identification of mosquito specimens in rural Uganda. Upcoming design updates will address the concerns of users and observers.

Affordance of a learning application for supporting student’s development of academic literacy

This paper takes as its starting point the problem area that deals with how students at university can develop academic literacy. One of the methods is through learning technology. This paper examines what affordance a new developed learning technology - the study app - has for developing student learning technology. The RQ is: what are the affordances of a learning application and its integrated design of learning tools in order to support student’s development of academic literacy? The study app is an example of technology enhances learning, where technology supports operational improvement in teaching and learning because students with the study app can experience a ubiquitous learning environment, designed on basis of user studies that are designed to be integrated in student’s everyday life. The question is whether a learning technology also supports the student’s activity oriented toward developing study competences and becoming more academic literate? The background for this research is a project of development and designing an ICT based learning application for supporting student’s development of study skills. The project is supported by the Faculty of Humanities at University of Southern Denmark and is organized in a project group related to department of Design and Communication. The purpose of the learning application is that it: 1) shall supports students in developing and enhancing study skills, 2) that it is relevant and motivating for users to use, 3) that it a new proposal in relation to existing solutions. The study is based on Leontiev's theory of activity and its three levels - activities, actions and operations - which provide a systematic insight into the interaction of learners with a learning technology in order to develop their study skills. The paper examines here various learning contexts that are defined as central to study skills and which learning technology seeks to support: a general study competence, a concrete study competence and a link between study and future work. Furthermore, the learning theory on which learning technologies are designed is also examined. The paper concludes with a discussion of technology enhanced learning and which type of enhancement of learning that can be supported by learning technology.