Cyber-Democracy or Cyber-Hegemony? Exploring the Political and Economic Structures of the Internet as an Alternative Source of Information

Although government regulation of the Internet has been decried as undercutting free speech, the control of Internet content through capitalist gateways???namely, profit-driven software companies???has gone largely uncriticized. The author argues that this discursive trend manufactures consent through a hegemonic force neglecting to confront the invasion of online advertising or marketing strategies directed at children. This study suggests that ???inappropriate content??? (that is, nudity, pornography, obscenities) constitutes a cultural currency through which concerns and responses to the Internet have been articulated within the mainstream. By examining the rhetorical and financial investments of the telecommunications business sector, the author contends that the rhetorical elements creating ???cyber-safety??? concerns within the mainstream attempt to reach the consent of parents and educators by asking them to see some Internet content as value laden (sexuality, trigger words, or adult content), while disguising the interests and authority of profitable computer software and hardware industries (advertising and marketing). Although most online ???safety measures??? neglect to confront the emerging invasion of advertising/marketing directed at children and youth, the author argues that media literacy in cyberspace demands such scrutiny. Unlike measures to block or filter online information, students need an empowerment approach that will enable them to analyze, evaluate, and judge the information they receive.published or submitted for publicatio

The Volume 21, Issue 21, April 22, 1988

Stories: Courier Crowned Best In State Enrollment Jump Spurs More Mid-Day Classes Forensics Wins Two-Year Nationals, Heads For Four-Year Tournament Feiffer’s Fables Fracture Political Panorama People: Jules Feiffe

MAVERICK: A Synthetic Murder Mystery Network Dataset to Support Sensemaking Research

AbstractThe MAVERICK dataset was created to support a series of empirical studies looking at the effectiveness of network visualizations intended to support information foraging and human sensemaking within the domain of counterinsurgency intelligence analysis. This synthetic dataset is structured as a forensic mystery with the central goal of solving a fictional murder. The dataset includes 181 text-based reports, with additional media included with some messages as attachments, collected from various sources of varying reliability. The reports are framed as being collected from the perspective of a reporter investigating the murder through interviews with suspects and observations taken at the site the murder. The dataset includes intentional and unintentional deception along with calculated source reliabilities based on available evidence. The dataset is dynamic in nature, as the information in the dataset evolves and expands over a simulated period of time. This is done to both to simulate a real-world scenario, and to allow for evolutionary tasks and experiments to be performed using the dataset. The dataset is designed to be complex enough to simulate a real-world, while remaining accessible to individuals without experience in a specific domain of interest. This meant that it had to be on a topic that did not require prior domain knowledge to understand available information or to understand what strategies should be applied during analysis of the dataset. The solution to these challenges was the development of a fictional murder mystery. The plot involves a murder that took place over the course of a weekend with several possible suspects at a large private estate. This scenario allowed for a great deal of complexity; however, it was also a subject matter that could be easily understood by participants without prerequisite domain experience

The Effects of the Social-Listener Protocol on the Observing, Helping, and Vocal Behavior of Children with ASD

Psychologists have long been interested in the study and development of empathy, though there has often been variation in the literature in regards to definition and measurement (Wispé 1986). Nevertheless, researchers in the field do agree that empathy is an essential social skill with evolutionary roots (De Waal, 2008). Yet, findings have shown that this stimulus control does not readily develop for all individuals; one such population is individuals with Autism Spectrum Disorder (ASD). The purpose of this study was to provide a behavioral measurement to determine if children with an educational classification of ASD would demonstrate empathy in an unfair play scenario and, if empathetic behavior is absent, can the Social-Listener Protocol (SLR) intervention result in the emergence of empathy. In Experiment I, the participants were placed in an unfair free play scenario and data were recorded on empathetic behavior. I selected 11 participants, from a mainstream first- and second-grade classroom, and two self-contained classrooms. Three participants from a mainstream second grade classroom were recruited to be actors in the free-play session. In this free play setting the participant was given an item while a peer, functioning as the confederate, was told there that there was not enough for him/her. Data were recorded for the participant’s observing and helping behavior. In addition, the participant was asked four empathy questions following the experiment. The results showed that, overall, participants with ASD demonstrated less observing behavior and answered fewer empathy-related questions correctly when compared to their typically developing peers. However, participants with ASD did not differ from their typically developing peers in regards to empathetic behavior. In Experiment II, I conducted additional free play probes across three activities. These free play settings differed from that of Experiment I as the child with ASD was given a Ziploc bag with multiple items, as opposed to one item. Data were collected on the number of times the participant looked at the peer, the vocal verbal operants emitted, and the number of seconds the participant shared the item. The results overall showed low levels of vocal verbal operants and sharing across participants. A multiple probe design was used to test the effects of the SLR protocol on empathetic behavior. The SLR protocol was composed of four activities that yoked the participant with a peer, against the teacher, to access a reinforcer. The results showed an increase in vocal verbal operants for Participant 3, 4, and 6. In addition, the results demonstrated an increase in sharing for Participants 2, 3, 4, and 6. Interestingly, the results showed an increase in correct responses to empathetic questions for Participants 2, 3, and 4 as well. The results show no significant change for Participants 1, 2, and 5. In Experiment III, I conducted additional phases of the SLR protocol and paired Participants 1, 2, and 5 with the same peer for intervention and probe sessions. In addition, I conducted observational learning probes. The results showed that Participants 1, 3, 4, and 6 had observational learning in their repertoire. Post-intervention results show an increase in vocal verbal operants, sharing behavior, and the induction of observational learning for Participant 2. There was no significant change for Participants 1 and 5

Creating Supplimental Visual and Manipulative Activities for Biology

Alternative visual and tactile teaching strategies and their educational effectiveness for students of diverse backgrounds in the life sciences were studied. Supplemental activities to traditional, commercial, science programs were found to provide many effective learning pathways to a wide spectrum of learning styles. To assist in providing additional learning approaches toward program goals, the educational theory in the literary review was applied to the formation of a set of classroom science materials. Suggestions for classroom use have been provided

Fluid Intelligence is Key to Successful Cryptic Crossword Solving

British-style cryptic crossword solving is an under-researched domain of expertise, relatively unburdened by confounds found in other expertise research areas, such as early starting age, practice regimes, and high extrinsic rewards. Solving cryptic crosswords is an exercise in code-cracking detection work, requiring the segregation and interpretation of multiple clue components, and the deduction and application of their controlling rules. Following the Grounded Expert Components Approach (GECA, Friedlander & Fine, 2016) an earlier survey demonstrated that solvers were typically educated to at least degree level, often in mathematics and science-related disciplines. This study therefore hypothesized that as a group they would show higher-than-average fluid intelligence compared to a general population, with experts showing higher levels than ordinary solvers. Twenty-eight crossword solvers (18 objectively defined experts, and 10 non-experts) solved a bespoke cryptic crossword and completed the Alice Heim tests of fluid intelligence (AH5), a timed high-grade test, measuring verbal and numerical (Part I) and diagrammatic (Part 2) reasoning abilities. In the 45m allowed, 17 experts and 2 non-experts correctly finished the crossword (times ranging between 11m and 40m). Both solver groups scored highly on the AH5 (both overall and for Part I) compared to manual test norms, suggesting that cryptic crossword solving has a high cognitive entry threshold. The experts scored higher than the non-experts, both overall (p = .032) and on Part I (p = .002). The overall and Part I AH5 scores correlated negatively (rs =-.48;-.72 respectively) with extrapolated finishing times: faster finishing time being associated with higher AH5 scores. The experts and non-experts were matched in age, education, crossword solving experience, and weekly hours spent solving, leading to the suggestion that fluid intelligence differences between the groups may play an important role in cryptic crossword solving expertise. Although small in scale, the study thus adds to the growing body of literature which challenges the "deliberate practice only" framework of high expertise in a performance domain. Suggestions for future explorations in this domain are made

Understanding the Role of Interactivity and Explanation in Adaptive Experiences

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating interactivity and explanation through four studies. Study I takes the first step to link the explanation and interactivity in machine learning systems to facilitate users\u27 engagement with the underlying machine learning model with the Tic-Tac-Toe game as a use case. The results show that explainable machine learning (XML) systems (and arguably XAI systems in general) indeed benefit from mechanisms that allow users to interact with the system\u27s internal decision rules. Study II, III, and IV further focus on adaptive experiences in recommender systems in specific, exploring the role of interactivity and explanation to keep the user “in-the-loop” in recommender systems, trying to mitigate the ``filter bubble\u27\u27 problem and help users in self-actualizing by supporting them in exploring and understanding their unique tastes. Study II investigates the effect of recommendation source (a human expert vs. an AI algorithm) and justification method (needs-based vs. interest-based justification) on professional development recommendations in a scenario-based study setting. The results show an interaction effect between these two system aspects: users who are told that the recommendations are based on their interests have a better experience when the recommendations are presented as originating from an AI algorithm, while users who are told that the recommendations are based on their needs have a better experience when the recommendations are presented as originating from a human expert. This work implies that while building the proposed novel movie recommender system covered in study IV, it would provide a better user experience if the movie recommendations are presented as originating from algorithms rather than from a human expert considering that movie preferences (which will be visualized by the movies\u27 emotion feature) are usually based on users\u27 interest. Study III explores the effects of four novel alternative recommendation lists on participants’ perceptions of recommendations and their satisfaction with the system. The four novel alternative recommendation lists (RSSA features) which have the potential to go beyond the traditional top N recommendations provide transparency from a different level --- how much else does the system learn about users beyond the traditional top N recommendations, which in turn enable users to interact with these alternative lists by rating the initial recommendations so as to correct or confirm the system\u27s estimates of the alternative recommendations. The subjective evaluation and behavioral analysis demonstrate that the proposed RSSA features had a significant effect on the user experience, surprisingly, two of the four RSSA features (the controversial and hate features) perform worse than the traditional top-N recommendations on the measured subjective dependent variables while the other two RSSA features (the hipster and no clue items) perform equally well and even slightly better than the traditional top-N (but this effect is not statistically significant). Moreover, the results indicate that individual differences, such as the need for novelty and domain knowledge, play a significant role in users’ perception of and interaction with the system. Study IV further combines diversification, visualization, and interactivity, aiming to encourage users to be more engaged with the system. The results show that introducing emotion as an item feature into recommender systems does help in personalization and individual taste exploration; these benefits are greatly optimized through the mechanisms that diversify recommendations by emotional signature, visualize recommendations on the emotional signature, and allow users to directly interact with the system by tweaking their tastes, which further contributes to both user experience and self-actualization. This work has practical implications for designing adaptive experiences. Explanation solutions in adaptive experiences might not always lead to a positive user experience, it highly depends on the application domain and the context (as studied in all four studies); it is essential to carefully investigate a specific explanation solution in combination with other design elements in different fields. Introducing control by allowing for direct interactivity (vs. indirect interactivity) in adaptive systems and providing feedback to users\u27 input by integrating their input into the algorithms would create a more engaging and interactive user experience (as studied in Study I and IV). And cumulatively, appropriate direct interaction with the system along with deliberate and thoughtful designs of explanation (including visualization design with the application environment fully considered), which are able to arouse user reflection or resonance, would potentially promote both user experience and user self-actualization