Personalized content recommendations on smart TV: Challenges, opportunities, and future research directions

Wireless Communications and Mobile Computing

et al. 2022

Self Cite

The ubiquitous devices and technologies to support teachers and students in a learning environment include the Internet of things (IoT), learning analytics (LA), augmented or virtual reality (AR/VR), ubiquitous learning environment (ULE), and wearables. However, most of these solutions are obtrusive, with substantial infrastructure costs and pseudo-real-time results. Real-time detection of students’ activeness, participation, and activity monitoring is important, especially during a pandemic. This research study provides a low-cost teacher orchestration solution with real-time results using off-the-shelf devices. The proposed solution determines a teacher’s activeness using multimodal data (MMD) from both teacher and student’s devices. The MMD extracts different features from data, decodes them, and displays them to the instructor in real time. It allows the instructor to update their teaching methodology in real time to get more students on board and provide a more engaging learning experience. Our experimental results show that real-time feedback about the classroom’s current status helped improve learning outcomes by about 45%. Also, we investigated a 50% increase in classroom engaging experience.

Section: Ubiquitous Computing and Othermentioning

confidence: 99%

Towards a Low-Cost Teacher Orchestration Using Ubiquitous Computing Devices for Detecting Student’s Engagement

Ahmad

Wireless Communications and Mobile Computing

et al. 2022

Self Cite

“…However, most users cannot utilize all these functions properly due to complexity, clutter, and poor UI design [1]. A smart TV is a lean-back and shared device for individuals and family members [2]. Various applications have been developed for smart TVs that provide multiple services and functionalities [2][3][4], such as video-on-demand services, online and offline games, live channels, movies, dramas, TV shows, Web browsing, social networking, and recommendation.…”

Section: Introductionmentioning

confidence: 99%

“…A smart TV is a lean-back and shared device for individuals and family members [2]. Various applications have been developed for smart TVs that provide multiple services and functionalities [2][3][4], such as video-on-demand services, online and offline games, live channels, movies, dramas, TV shows, Web browsing, social networking, and recommendation. e latest smart TVs are also embedded with branded operating systems.…”

Section: Introductionmentioning

confidence: 99%

Perspectives on the Design, Challenges, and Evaluation of Smart TV User Interfaces

Khan

et al. 2022

Scientific Programming

Self Cite

The user interface (UI) is a primary source of interaction with a device. Since the introduction of graphical user interface (GUI), software engineers and designers have been trying to make user-friendly UIs for various computing devices, including smartphones, tablets, and computers. The modern smart TV also comes with built-in operating systems. However, little attention has been given to this prominent entertainment device, i.e., smart TV. The technological advancement and proliferation of smart TV enabled the manufacturer to provide rich functionalities and features; however, this richness resulted in more clutter and attention-demanding interfaces. Besides, smart TV is a lean-back supporting device having a diverse range of users. Therefore, smart TV’s usability and user experience (UX) are questionable due to diverse user interests and limited features of traditional remote controls. This study aimed to discuss and critically analyze the features and functionalities of the existing well-known smart TV UIs of various operating systems in the context of usability, cognition, and UX. Moreover, this study highlights the issues and challenges in the current smart TV UIs and recommends some research opportunities to cope with the smart TV UIs. This study further reports and validates some overlooked factors affecting smart TV UIs and UX. A subjective study and usability tests from diverse users are presented to validate these factors. The study concludes that a one-size-fits-all UI design is unsuitable for shared devices, i.e., smart TV. This study further recommends a personalized adaptive UI, which may enhance the learnability and UXs of the smart TV viewers.

“…The implicit feedback are navigation and type of sites watching, whereas explicit feedback are likes, dislikes, ratings, and keywords. In hybrid approaches, we combine all these approaches [20].…”

Section: Introductionmentioning

confidence: 99%

Interest-Based Content Clustering for Enhancing Searching and Recommendations on Smart TV

Jan

Wireless Communications and Mobile Computing

et al. 2022

Self Cite

Smart TV has become a pervasive device due to its support for numerous entertainment options. These capabilities of smart TV make it attractive for viewers and researcher. Besides, a plethora of multimedia content continues to grow, which makes searching and browsing the desired content a difficult, time-consuming, and contributes to cognitive overload problem. In the case of smart TV, making clusters of the related content based on user’s interest is among the best solutions. In this connection, this study proposed a dynamic approach for clustering the TV-related online multimedia content and presenting them in a manageable format on smart TV to mitigate the issue of searching and relevant recommendations. We collected and clustered the content from diverse data sources based on the viewer’s interest. This further recommends novel content to the viewers without social metadata, such as rates, tags, which is normally insignificant in for smart TV viewership due to its shared nature. We used bisecting K -means, Lingo, and Suffix Tree Clustering (STC) algorithms. A comparative analysis of these algorithms and suitability in the context of smart TV is also presented. Results show that the proposed approach enhances search results and recommends relevant content based on user’s interests.