User Interface Plasticity: Model Driven Engineering to the Limit!

Keynote paper.International audienceTen years ago, I introduced the notion of user interface plasticity to denote the capacity of user interfaces to adapt, or to be adapted, to the context of use while preserving usability. The Model Driven Engineering (MDE) approach, which was used for user interface generation since the early eighties in HCI, has recently been revived to address this complex problem. Although MDE has resulted in interesting and convincing results for conventional WIMP user interfaces, it has not fully demonstrated its theoretical promises yet. In this paper, we discuss how to push MDE to the limit in order to reconcile high-level modeling techniques with low-level programming in order to go beyond WIMP user interfaces

Towards developing an instrument in measuring the need for InfoVis

The increasing trend of data volume and its superabundance have been endangering institutional data and specifically, higher education institutions (HEI) students’ data.The most challenging is the need for HEI to make sense from their large datasets, through gaining insights and understanding the pattern and trends of events therein. Deductions from extant literatures strongly indicate the presence of information overload as the factor constraining HEI decision makers from making wealthy use of the institutional datasets.However, no study has empirically investigated the presence of information overload in HEI students’ data management.To attend to this, this study aims at developing an instrument to be used in measuring the presence of information overload, and justifiably, the need for Information Visualization (InfoVis) –being an argued better tool for institutional data management. This study employs quantitative research method withadministration of 9-item survey questionnaire. Thirty-two (32) respondents are purposively drawn among HEI decision makers.Descriptive statistics is used as the statistical technique to find the mean valueof the computed variable based on the normal Likert 5-point scale.The result of the instrument reliability testgives a value of 0.712 as the Cronbach’s Alpha value which suggests that the items designed are internally consistent, and a weighted mean value of 4.03 strongly supportsthe hypothesis that HEI experiences information overload

Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions

Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while interacting with students‟ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which will birth the domain‟s InfoVis tool. This study therefore aims to design HEI Students‟ data-focused InfoVis (HSDI) conceptual design framework which addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a users‟ study to investigate, elicit and prioritize the students‟ data-related explicit knowledge preferences of HEI domain policy. The corresponding students‟ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the users‟ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domain‟s explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniques‟ model with its design process–as identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and users‟ experience evaluation. For an InfoVis that will appropriately present and represent the domain explicit knowledge preferences, support the students‟ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques, 2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI students‟ data usage process

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

Identifying Inexpensive Off-the-Shelf Laser Pointers for Multi-User Interaction on Large Scale Displays

We present a method for identifying inexpensive, off-the-shelf laser pointers in a multiuser interaction environment on large-scale displays. We identify a laser pointer\u27s personality, a measure of its output in a particular context. Our method requires a set of inexpensive and unmodified green lasers, a large screen, a projector, and a camera with an infrared (IR) filter. The camera detects the IR spillover from the green laser beam, while ignoring color information projected onto the screen. During a calibration phase, a radial histogram of each laser\u27s IR spillover are used to represent the laser\u27s personality. Our system is able to identify the spots of a specific laser, allowing multiple users to simultaneously interact in the environment. In addition, we present a series of applications that take advantage of tracked and identified laser pointers to demonstrate large-scale, multiuser interactions

A Human-Centric System for Symbolic Reasoning About Code

While testing and tracing on specific input values are useful starting points for students to understand program behavior, ultimately students need to be able to reason rigorously and logically about the correctness of their code on all inputs without having to run the code. Symbolic reasoning is reasoning abstractly about code using arbitrary symbolic input values, as opposed to specific concrete inputs. The overarching goal of this research is to help students learn symbolic reasoning, beginning with code containing simple assertions as a foundation and proceeding to code involving data abstractions and loop invariants. Toward achieving this goal, this research has employed multiple experiments across five years at three institutions: a large, public university, an HBCU (Historically Black Colleges and Universities), and an HSI (Hispanic Serving Institution). A total of 862 students participated across all variations of the study. Interactive, online tools can enhance student learning because they can provide targeted help that would be prohibitively expensive without automation. The research experiments employ two such symbolic reasoning tools that had been developed earlier and a newly designed human-centric reasoning system (HCRS). The HCRS is a first step in building a generalized tutor that achieves a level of resolution necessary to identify difficulties and suggest appropriate interventions. The experiments show the value of tools in pinpointing and classifying difficulties in learning symbolic reasoning, as well as in learning design-by-contract assertions and applying them to develop loop invariants for code involving objects. Statistically significant results include the following. Students are able to learn symbolic reasoning with the aid of instruction and an online tool. Motivation improves student perception and attitude towards symbolic reasoning. Tool usage improves student performance on symbolic reasoning, their explanations of the larger purpose of code segments, and self-efficacy for all subpopulations

Intelligent Interfaces to Empower People with Disabilities

Severe motion impairments can result from non-progressive disorders, such as cerebral palsy, or degenerative neurological diseases, such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), or muscular dystrophy (MD). They can be due to traumatic brain injuries, for example, due to a traffic accident, or to brainste