This study presents an intersectional analysis of beliefs about and experiences of Black women regarding sexual violence, using focus group and survey methods with BIPOC college students ( N = 37). Based on the analysis, we propose a theoretical model of “(un)victimization” that integrates two inequality frames – misogynoir and legal cynicism – applied to sexual assault. Specifically, this model explains the experiences of Black women survivors of sexual violence who simultaneously experience sexual victimization, are denied legitimate victim status, and have reason to distrust legal forms of justice. We theorize the process of (un)victimization enables future sexual assault of Black women.

Despite efforts by feminists to educate people about healthy sexual interactions and to promote the benefits of affirmative consent, a heterogendered imbalance in sexual intimacy persists. Much of the societal and research attention has focused on clear cases of nonconsensual sex, but a wider lens that incorporates social pressures and coercion is needed. (Mostly) cis-men continue to pressure and coerce their partners (mostly people who identify as women) to acquiesce to sexual intimacy. Our heteropatriarchal culture continues to perpetuate the belief that men are owed sex from women in many situations. Feminist scholars have argued that, instead of a stark line between consensual and nonconsensual sex, there is a continuum or spectrum ranging from sexual consent to sexual assault, creating a large “grey area” in which partners must navigate sexual intimacy. This grey area is not gender neutral. Gendered structure, culture, discourse, and practices help to normalize heterogendered dominance in everyday life, undermining women’s sexual agency while also mobilizing rape. Drawing on interviews with university students (N=45) who have navigated this spectrum, we seek to map the grey area, exploring how consent is often hijacked through relentless pressure and coercion. When pressure and coercion are encoded into the gendered order as entitlements granted to men, the line between sexual assault and agentic, “consensual” sex becomes less and less discernible. We conclude that, in order to foster sexual autonomy, gendered power dynamics must be disentangled from sexual intimacy.

The #MeToo movement has shined light on sexual harassment and assault, creating new avenues for survivors to seek justice, outside of the justice system. People (mostly men) accused of sexual assault or harassment are publicly 'outed', and the consequence have been serious for many. #MeToo stresses that 'rape is a man's issue', arguing that men can end rape by educating themselves about gendered power, changing their behaviour, and holding other men accountable. In this contentious context, affirmative consent policies are taking root in US universities. In this paper, we ask, How does gender frame the negotiation of consent? We analyse data from our interview project on sexual consent (N = 45) to explore gendered power dynamics in subjects' reported negotiations of sexual consent. We find that participants' understandings of consent reinforced - rather than destabilized - hegemonic systems of power. Even when acting in ways that seemed consistent with feminist conceptualizations of bodily autonomy and affirmative consent, men in this study did so to protect their own interests. Affirmative consent was mediated through gender frames that stressed men's sexual entitlement. We conclude that sexual assault intervention strategies need to be reworked to address systemic, cultural, and individual-level issues.

This article reports the results of a randomized control trial of a semester-long intervention designed to promote ninth-grade science students’ use of text-based investigation to create explanatory models of biological phenomena. The main research question was whether the student participants in the intervention outperformed the students in the control classes, as assessed by several measures of comprehension and application of information to modeling biological phenomena not covered in the instruction. A second research question examined the impact on the instructional practices of the teachers who implemented the intervention. Multilevel modeling of outcome measures, controlling for preexisting differences at individual and school levels, indicated significant effects on the intervention students and teachers relative to the controls. Implications for classroom instruction and teacher professional development are discussed.

This article describes several approaches to assessing student understanding using written explanations that students generate as part of a multiple-document inquiry activity on a scientific topic (global warming). The current work attempts to capture the causal structure of student explanations as a way to detect the quality of the students’ mental models and understanding of the topic by combining approaches from Cognitive Science and Artificial Intelligence, and applying them to Education. First, several attributes of the explanations are explored by hand coding and leveraging existing technologies (LSA and Coh-Metrix). Then, we describe an approach for inferring the quality of the explanations using a novel, two-phase machine-learning approach for detecting causal relations and the causal chains that are present within student essays. The results demonstrate the benefits of using a machine-learning approach for detecting content, but also highlight the promise of hybrid methods that combine ML, LSA and Coh-Metrix approaches for detecting student understanding. Opportunities to use automated approaches as part of Intelligent Tutoring Systems that provide feedback toward improving student explanations and understanding are discussed.

Prior research has shown that students learn from Intelligent Tutoring Systems (ITS). However, students' attention may drift or become disengaged with the task over extended amounts of instruction. To remedy this problem, researchers have examined the impact of game-like features (e.g., a narrative) in digital learning environments on motivation and learning. Some of this research has concluded that the game-like features decrease learning because the features take away resources from the primary task of learning subject-matter content. However, these experiments have involved short-term interventions of less than an hour. Two experiments using college students examined the impact of adding game-like features to the ITS AutoTutor in an intervention that lasted 4 h. In one study, a game-like version was compared to a text-only version and a "do nothing'' control. In another study, a game-like version was compared to a nongame version that had similar interfaces. Unlike prior research that has shown that narratives decrease learning in digitally-based learning environments, the game-like features, which included a narrative, had little impact on learning from the ITS. Reasons for the discrepancies are discussed.

Educational standards put a renewed focus on strengthening students' abilities to construct scientific explanations and engage in scientific arguments. Evaluating student explanatory writing is extremely time-intensive, so we are developing techniques to automatically analyze the causal structure in student essays so that effective feedback may be provided. These techniques rely on a significant training corpus of annotated essays. Because one of our long-term goals is to make it easier to establish this approach in new subject domains, we are keenly interested in the question of how much training data is enough to support this. This paper describes our analysis of that question, and looks at one mechanism for reducing that data requirement which uses student scores on a related multiple choice test.

Aggressive behavior often occurs despite salient cues within the immediate environment that indicate aversive consequences will likely follow. Prior research has shown high trait aggressiveness to be related to sensitivity to situational provocation; however, little research has examined whether it is also related to insensitivity to situational inhibitors. This study examines the relationship between trait aggressiveness and aggressive behavior in a provocative context with, and without, an unambiguous inhibitory stimulus. Prior to experiencing provocation and being afforded the opportunity to retaliate, participants who varied in trait aggressiveness were explicitly given (or not given) an instruction that aggressive behavior might lead to aversive consequences and, thus, one should not behave aggressively. Findings revealed that without the instruction, those higher in trait aggressiveness exhibited steeper increases in aggressive responding as provocation increased. In the group that received the instruction, trait aggressiveness was unrelated to aggressive responding at all levels of provocation.

In the US in particular, there is an increasing emphasis on the importance of science in education. To better understand a scientific topic, students need to compile information from multiple sources and determine the principal causal factors involved. We describe an approach for automatically inferring the quality and completeness of causal reasoning in essays on two separate scientific topics using a novel, two-phase machine learning approach for detecting causal relations. For each core essay concept, we initially trained a window-based tagging model to predict which individual words belonged to that concept. Using the predictions from this first set of models, we then trained a second stacked model on all the predicted word tags present in a sentence to predict inferences between essay concepts. The results indicate we could use such a system to provide explicit feedback to students to improve reasoning and essay writing skills.

The purpose of this study was to examine online processes used during reading that may contribute to comprehension. One hundred twenty-four fourth-grade students with proficient decoding but poor reading comprehension skills responded to narrative and informational texts using a thinkaloud procedure. Analysis of think-aloud transcripts revealed that readers made significantly more textbased connections while reading narratives and more knowledge-based connections while reading informational texts. Findings have implications for further research on reading comprehension processes. Novice Literary Interpretations: Prompting and Processing Matter Candice Burkett, Susan R. Goldman Abstract. Research suggests literary novices are inept at interpreting literary works (Graves & Frederiksen, 1991; Zeitz, 1994). The current study investigated novices’ literary interpretations for a short story. Results indicated more interpretations when prompted than during initial reading despite evidence of elaborative processing, attention to literary devices, and adequate story comprehension. Furthermore, elaborative processing was positively related to interpretations. These results implicate differences between experts and novices in what is entailed in “reading” a literary work.

The Internet, and all the netcentric innovations that emerge from it, have transformed the workplace and our working lives in a very short time. The net added a window to the world on worker's desks, and made 24 by 7 connectivity to the workplace a reality--blurring the line between work and time off. It triggered new styles of teamwork, new leadership challenges, new modes of communicating, new job roles and employer-employee relationships, and new, alarmingly effective tools for workplace surveillance. The capabilties offered by netcentric technologies might seem to eliminate completely the need for a physical workplace, but the workplace remains, partly because the virtual, and in fact, the physical appearance of a typical office looks about the same. Nevertheless, the psychological characteristics of the workplace have changed considerably. Workers, from the mail room clerk to the CEO, are learning new skills--to employ on the net's power but avoid the egregious blunders that the net so dramatically amplifies. In The Internet in the Workplace, Patricia Wallace demonstrates how netcentric technologies touch every kind of workplace, and explores the challenges and dilemmas they create. Patricia Wallace is Director, Information Technology and Distance Programs at the Center for Talented Youth, Johns Hopkins University. Wallace's background and career span the disciplines of information technology, psychology, education, and business. Her recent book, The Psychology of the Internet (Cambridge, 1999) has been translated into nine languages. Wallace's work has been featured often in the media, including MSNBC, CNN, ABC News, the BBC, NPR, USA Today, and the Washington Post.

Cognitive disequilibrium and its affiliated affective state of confusion have been found to positively correlate with learning, presumably due to the effortful cognitive activities that accompany their experience. Although confusion naturally occurs in several learning contexts, we hypothesize that it can be induced and scaffolded to increase learning opportunities. We addressed the possibility of confusion induction in a study where learners engaged in trialogues on research methods concepts with animated tutor and student agents. Confusion was induced by staging disagreements and contradictions between the animated agents, and then inviting the human learners to provide their opinions. Self-reports of confusion indicated that the contradictions were successful at inducing confusion in the minds of the learners. A second, more objective, method of tracking learners' confusion consisted of analyzing learners' performance on forced-choice questions that were embedded after contradictions. This measure was also found to be revealing of learners' underlying confusion. The contradictions alone did not result in enhanced learning gains. However, when confusion had been successfully induced, learners who were presented with contradictions did show improved learning compared to a no-contradiction control. Theoretical and applied implications along with possible future directions are discussed.

OperationARIES! (or ARIES for short) is an intelligent tutoring system that teaches critical thinking and helps learners acquire scientific inquiry skills. One of the core components of ARIES is “trialogs” which are three-party conversations in natural language among a human student and two artificial pedagogical agents (tutor and fellow student). These tutorial interactions were designed to enhance the students’ learning experience. Assessing student input is essential to the performance of ARIES. Regular expressions and Latent Semantic Analysis (LSA) were used in models that evaluate students’ answers. The resulting computational models were found to be as reliable as human raters. Keywords-Operation ARIES!; AutoTutor; intelligent tutoring systems; LSA; natural language processing

Cognitive disequilibrium and its affiliated affective state of confusion have been found to be beneficial to learning due to the effortful cognitive activities that accompany their experience. Although confusion naturally occurs during learning, it can be induced and scaffolded to increase learning opportunities. We addressed the possibility of induction in a study where learners engaged in trialogues on critical thinking and scientific reasoning topics with animated tutor and student agents. Confusion was induced by staging disagreements and contradictions between the animated agents, and the (human) learners were invited to provide their opinions. Self-reports of confusion and learner responses to embedded forced-choice questions indicated that the contradictions were successful at inducing confusion in the minds of the learners. The contradictions also resulted in enhanced learning gains under certain conditions.

Aggressive responding following benzodiazepine ingestion has been recorded in both experimental and client populations, however, the mechanism responsible for this outcome is unclear. The goal of this study was to identify an affective concomitant linked to diazepam-induced aggression that might be responsible for this relationship. Thirty males (15 diazepam and 15 placebo) participated in the Taylor Aggression Paradigm while covertly being videotaped. The videotapes were analyzed using the Facial Action Coding System with the goal of identifying facial expression differences between the two groups. Relative to placebo participants, diazepam participants selected significantly higher shock settings for their opponents, consistent with past findings using this paradigm. Diazepam participants also engaged in significantly fewer appeasement expressions (associated with the self-conscious emotions) during the task, although there were no group differences for other emotion expressions or for movements in general.

Essays are an important measure of complex learning, but pronouns can confound an author's intended meaning for both readers and text analysis software. This descriptive investigation considers the effect of pronouns on a computer-based text analysis approach, ALA-Reader, which uses students' essays as the data source for deriving individual and group knowledge representations. Participants in an undergraduate business course (n = 45) completed an essay as part of the course final examination. The investigators edited the essays to replace the most common pronouns (their, it, and they) with the appropriate referent. The original unedited and the edited essays were processed with ALA-Reader using two different approaches, sentence and linear aggregate. These data were then analyzed using a Pathfinder network approach. The average group network similarity values comparing the original to the edited essays were large (i.e., about 90% overlap) but the linear aggregate approach obtained larger values than the sentence aggregate approach. The linear aggregate approach also provided a better measure of individual essay scores (e.g., r = 0.74 with composite rater scores). This data provides some support that the ALA-Reader linear approach is adequate for capturing group knowledge structure representations from essays. Further development of the ALA-Reader approach is warranted.

ARIES (Acquiring Research Investigative and Evaluative Skills) is a computerized educational game in which players attempt to stop extraterrestrials from implicitly stunting scientific progress on Earth by publishing bad research in a variety of fields. Players progress through three modules: 1) read and be tested on an on-line science text, 2) evaluate potentially flawed research articles, and 3) learn question-asking skills. ARIES incorporates multiple learning principles, such as testing effects, generation effects, and formative feedback.