It is difficult for a student to succeed in a course without access to course materials and assignments; and yet, some students delay up to a month in obtaining access to these essential materials. Students delay buying material required for their course due to multiple reasons. Out of a concern for students with limited financial resources, some publishers offer a period of free courtesy access. But this may lead to students having access later in the course but then having a lapsed period until they pay for the materials after the courtesy access period ends. Not having key course materials early on probably hurts learning, but how much? In this paper, we investigate the question, "Does lack of access to instructional material impact student performance in blended learning courses?" Specifically, we analyze students who purchased and obtained access to online content at different points in the course. We determine that both types of failure to obtain access to course materials (delaying in signing up for the product, or signing up for a free trial and letting the trial period lapse without purchasing the materials) are associated with substantially worse student outcomes. Students who purchased the product within the first few days of class had the best scores (median 77). Those who waited two weeks before accessing the product did the worst (median 56, effect size Cliff's Delta=0.31 1). We conclude with a discussion of possible interventions and actions that can be taken to ameliorate the situation.

Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis.

Recent growth in broadband access and proliferation of small personal devices that capture images and videos has led to explosive growth of multimedia content available everywhere - from personal disks to the Web. While digital media capture and upload has become nearly universal with newer device technology, there is still a need for better tools and technologies to search large collections of multimedia data and to find and deliver the right content to a user according to her current needs and preferences. A renewed focus on the subjective dimension in the multimedia lifecycle, from creation, distribution, to delivery and consumption, is required to address this need beyond what is feasible today. Integration of the subjective aspects of the media itself - its affective, perceptual, and physiological potential (both intended and achieved), together with those of the users themselves will allow for personalizing the content access, beyond today's facility. This integration, transforming the traditional multimedia information retrieval (MIR) indexes to more effectively answer specific user needs, will allow a richer degree of personalization predicated on user intention and mode of interaction, relationship to the producer, content of the media, and their history and lifestyle. In this paper, we identify the challenges in achieving this integration, current approaches to interpreting content creation processes, to user modelling and profiling, and to personalized content selection, and we detail future directions. The structure of the paper is as follows: In Section I, we introduce the problem and present some definitions. In Section II, we present a review of the aspects of personalized content and current approaches for the same. Section III discusses the problem of obtaining metadata that is required for personalized media creation and present eMediate as a case study of an integrated media capture environment. Section IV presents the MAGIC system as a case study of capturing effective descriptive data and putting users first in distributed learning delivery. The aspects of modelling the user are presented as a case study in using user's personality as a way to personalize summaries in Section V. Finally, Section VI concludes the paper with a discussion on the emerging challenges and the open problems.

The need for personalized summaries of media content has been driven by the recent and anticipated explosive growth in the media world. In this paper, we present a methodology and a supporting user study for generating user profiles and content features that can be used to automatically create personalized summaries of broadcast television content. We determined a mapping, from users' personality traits measured by commonly available personality tests, to computable video features that such personality traits appear to prefer. Three common personality profiles (Myers-Briggs, Merrill Reed, and Brain.exe) were elicited from 59 subjects, together with their preferred summary of news, music, and talk show videos. A factor analysis between the personality traits and the features in preferred summaries indicated that only some traits (e.g., gender, extroversion, control orientation, intuitiveness, etc.) and only some features (e.g., faces, reportage, text, chorus, host, etc.) had predictive value. The mapping of personality to feature also differed by genre. However, in general, extroverted users tended to prefer directly experienced content, while introverted users preferred content mediated through analysis. A validation user study is in progress

We present a system that summarizes the textual, audio, and video information of music videos in a format tuned to the preferences of a focus group of 20 users. First, we analyzed user-needs for the content and the layout of the music summaries. Then, we designed algorithms that segment individual song videos from full music video programs by noting changes in color palette, transcript, and audio classification. We summarize each song with automatically selected high level information such as title, artist, duration, title frame, and text as well as audio and visual segments of the chorus. Our system automatically determines with high recall and precision chorus locations, from the placement of repeated words and phrases in the text of the song's lyrics. Our Bayesian belief network then selects other significant video and audio content from the multiple media. Overall, we are able to compress content by a factor of 10. Our second user study has identified the principal variations between users in their choices of content desired in the summary, and in their choices of the platforms that should support their viewing.

Commercial detection plays an important role in various video segmentation and indexing applications. It provides high-level program segmentation so that other algorithms can be applied on the true program material in the broadcast. It is a challenge to have robust commercial detection methodology for various platforms, content formats, and broadcast styles that are used all over the world. Wide deployment of such an algorithm not only requires the development of new algorithms but also updating and tuning of parameters for existing algorithms. We present visual commercial detectors that rely on features including, luminance, letterbox, and keyframe distance. These detectors were developed after a careful study of the various features that can be extracted during MPEG-encoding process in real time. Due to the intermittent nature of the features, and platform restrictions, the commercial detection relies on a set of thresholds to keep the implementation as simple as possible. We evolved these thresholds using genetic algorithms (GAs) to optimize the performance. We show how a scalar genetic algorithm can locate sets of parameters in a multi-objective space (precision and recall) that outperform the values selected by an expert engineer. We present the results of optimizing a commercial detection algorithm for different data sets and parameter sets. In this paper we show that GAs drastically improved our approach and enabled fast prototyping and performance tuning of commercial detection algorithms.

The ability to summarize and abstract information is an essential part of intelligent behavior in consumer devices. However, even for the emerging area of video content analysis, user preferences on summarization have not been explored. This paper reports on a panel which asked: who, why and when summarization is needed; what information should be summarized; and what forms should summaries take. In particular, we investigated the requirements with sensitivity to user needs, user context, media content, device capabilities, and the methods by which user and environment profiles can be assembled and exploited. We organize our findings as a wish list containing four major user questions, and we report the current and near-term states of the art and the major technical challenges in satisfying them. Our study suggests that user preferences should be derived from explicit user statements and from implicit trends inferred from viewing histories and summary usage.

No abstract available.

Automatic indexing of large collections of multimedia data is important for enabling retrieval functions. Current approaches mostly draw on a single or dual modality of video content analysis. Here we describe a framework for the integration of multimedia content and context information, which generalizes and systematizes current methods. Content information in the visual, audio, and text domains, is described at different levels of granularity and abstraction. Context describes the underlying structural information that can be used to constrain the possible number of interpretations. We introduce a probabilistic framework that combines (a) Bayesian networks that describe both content and context and (b) hierarchical priors that describe the integration of content and context. We present an application that uses this framework to segment and index TV programs. We discuss experimental results on segment classification on six and a half hours of broadcast video. In our experiments we used audio context information. Classification results for financial segments yield 83% and for celebrity segments 89%.

The importance of video content analysis and retrieval increases as storage capacity grows in consumer devices. Videotext detection is very important for video indexing and retrieval enabling features such as commercial detection, intelligent keyframe extraction, program boundary detection and others. In this paper we propose multi-layered videotext detection tailored to an MP EG encoding scheme. We approached the videotext detection from a perspective of scalability and flexibility for different platforms with varying resources. We propose a three-layered algorithm. The first layer works in compressed domain features such as macroblock type. The second layer works in semicompressed domain such as DCT coefficients. The third layer works in uncompressed domain, i.e. spatial domain. As the next layer gets implemented the complexity increases but so does the precision of the algorithm. On constrained platforms, only the first layer would be implemented. On high-end platforms all three layers could be implemented to enable a full suite of indexing and retrieval applications.

Currently available personal video recorders find and store whole TV programs. Our system, Video Scouting, not only finds and stores programs; it automatically segments and indexes story segments from the programs according to viewers' profiles. The extracted descriptions serve the viewers' content information requests for program segment selection, e.g., play the three minute interview with Hillary Clinton. To achieve this, the system combines information from the audio, visual, and transcript domains in a probabilistic framework based on Bayesian networks. We describe the overall architecture, a system implementation, and discuss some experimental results.

We describe integrated multimedia processing for Video Scout, a system that segments and indexes TV programs according to their audio, visual, and transcript information. Video Scout represents a future direction for personal video recorders. In addition to using electronic program guide metadata and a user profile, Scout allows the users to request specific topics within a program. For example, users can request the video clip of the USA president speaking from a half-hour news program. Video Scout has three modules: (i) video pre-processing, (ii) segmentation and indexing, and (iii) storage and user interface. Segmentation and indexing, the core of the system, incorporates a Bayesian framework that integrates information from the audio, visual, and transcript (closed captions) domains. This framework uses three layers to process low, mid, and high-level multimedia information. The high-level layer generates semantic information about TV program topics. This paper describes the elements of the system and presents results from running Video Scout on real TV programs.

This paper describes the transcript triggers system and method that automatically retrieves information supplemental to video programming. The generated video transcript is searched for the items of interest and related information is returned. The system performs extraction of transcript, and matching of keywords based on a personal profile, pulling information from additional sources and handling the user interface using a rich set of association rules.

Current personal Vido recorders make it very easy for consumers to record whole TV programs. Our research however, focuses on personalizing TV at a sub-program level. We use a traditional Content-Based Information Retrieval system architecture consisting of archiving and retrieval modules. The archiving module employs a three-layered, multimodal integration framework to segment, analyze, characterize, and classify segments. The retrieval module relies on users personal preferences to deliver both full programs and video segments of interest. We tested retrieval concepts with real users and discovered that they see more value in segmenting non-narrative programs (e.g. news) than narrative programs (e.g. movies). We benchmarked individual algorithms and segment classification for celebrity and financial segments as instances of non-narrative content. For celebrity segments we obtained a total precision of 94.1% and recall of 85.7%, and for financial segments a total precision of 81.1% and a recall of 86.9%.

This paper describes the classification of a subset of printed or digitized Gujarati characters. Gujarati belongs to the genre of Devanagri scripts from the Indian subcontinent. Very little work is found in the literature for recognition of Indian language scripts. For this paper a subset of similar appearing Gujarati characters was chosen and subjected to classification by different classifiers. The sample and test images for the characters were obtained from digital images available on the Internet and from scanned images of printed Gujarati text. For their classification, the Euclidean Minimum Distance and the k-Nearest Neighbor classifiers were used with regular and invariant moments. The characters were also classified in the binary feature space using Hamming Distance classifier. The paper presents the recognition rates for these classifiers. A recognition rate of 67% is achieved.

In this paper we present a novel method for computing superhistograms to represent video segments. We build cumulative histograms for video shots and scenes. The underlying assumption is that a TV program has a consistent color palette, which can be derived as a family of merged individual shot histograms. We present the experimental results computed from several hours of different TV programs such as sitcoms, serials and news. The superhistograms representing episodes of the same sitcom look strikingly similar. The histograms extracted from TV news are not similar to the histograms extracted from sitcoms. However, there are superhistograms extracted from different sitcoms, which show certain resemblance to each other. This method can be used in a variety of applications that need video classification and retrieval methods such as video editing, studio archival, digital libraries, consumer products, and web crawling.

No abstract available.