ethanporcaro/eric-fulltext-meta huggingface dataset:
Using Comics for Climate Change in Science Education: Students&apos; Solutions and Aesthetic Subtleties
Author: Alp, Gamze
Date: 2023-00-00
Type: Journal Articles
Subjects: Climate,Science Education,Grade 5,Cartoons,Electronic Publishing,Electronic Books,Educational Technology,Pollution,Conservation (Environment),Natural Resources,Aesthetics,Story Telling,Foreign Countries,Secondary School Students

To constitute awareness of climate change, hearing the solutions of students in their words and seeing visual products by creating experiences in schools is vital. This case study was limited to sequential implementations of climate change in the natural sciences teaching process in 5th grade. After an education process on using Pixton, 12 students transformed their learning into digital comics. This research aimed to reveal the problems created by students through digital stories about climate change, solutions they suggested for climate problems, and aesthetic subtleties they applied to express themselves. Digital comics created by students, researcher notes, observations, and students' view forms were used as data collection tools. Finally, vital solutions to problems such as global climate change, pollution, destruction of the natural environment, and extinction of living creatures were created by students during this case. Important solutions consisting of preventing global warming, making peace with nature, vital measures and efforts to protect the soil were suggested. Besides, interesting aesthetic subtleties such as storification, positioning of the characters, place preferences, expression of emotions, and time depiction were applied. Teachers can use comics to see students' tendencies, raise awareness, encourage them, and help them develop solutions for current socioscientific problems.
The Chain Mediating Role of Perceived Family Support for Formal and Informal Science Learning in the Association between Family Socioeconomic Status and Informal Science Learning Experiences
Author: He, Xiang-xiang
Date: 2023-00-00
Type: Journal Articles
Subjects: Foreign Countries,Elementary School Students,Grade 4,Grade 5,Grade 6,Science Education,Learning,Informal Education,Family Programs,Socioeconomic Status,Learning Experience,Family Financial Resources,Social Cognition,Prediction

Students' informal science learning experiences are believed to strongly influence their attitudes toward science and their abilities in the subject. Factors associated with students' informal science learning experiences include family socioeconomic status and perceived family support. However, little is known about whether perceived family support for children's formal and informal science learning differs significantly. The present study filled this gap by exploring how students' perceived family support for formal/ informal science learning affected the link between their socioeconomic status and their experiences of informal science learning. Survey data were collected from 486 Chinese grade 4-6 primary school students. Structural equation modeling demonstrated that the students' socioeconomic status and their informal science learning experiences were significantly and positively correlated. This association was chain-mediated through two categories of perceived family support. Specifically, students' socioeconomic status affected their informal science learning experiences through the separate mediating role of family support for formal science learning and the sequential mediation of perceived family support for formal and informal science learning. These results uncover the role that family support plays in students' science education; accordingly, parents are encouraged to help their children learn science both formally and informally.
Reinventing Cybersecurity Internships during the COVID-19 Pandemic
Author: Sussman, Lori L.
Date: 2023-01-00
Type: Journal Articles
Subjects: Computer Security,Information Security,Internet,Computer Mediated Communication,COVID-19,Pandemics,Emergency Programs,Internship Programs,Distance Education,Electronic Learning,Professional Development,Curriculum,Crime Prevention,Crime,Skill Development,Professional Training

The Cybersecurity Ambassador Program provides professional skills training for emerging cybersecurity professionals remotely. The goal is to reach out to underrepresented populations who may use Federal Work-Study (FWS) or grant sponsored internships to participate. Cybersecurity Ambassadors (CAs) develop skills that will serve them well as cybersecurity workers prepared to do research, lead multidisciplinary, technical teams, and educate stakeholders and community members. CAP also reinforces leadership skills so that the next generation of cybersecurity professionals becomes a sustainable source of management talent for the program and profession. The remote curriculum innovatively builds non-technical professional skills (communications, teamwork, leadership) for cybersecurity research through student-led applied research and creating community-focused workshops. These student-produced workshops are in phishing, identity and privacy cyber safety, social media safety, and everyday home cyber safety. The CAs tailor the program to a particularly vulnerable population such as older adults, students, veterans, or similar people that make up most workshop participants. At this time, the data shows that this pedagogical approach to curriculum development, grounded in the Ground Truth Expertise Development Model (GTEDM), is a unique methodology. This curriculum teaches cybersecurity interns with key non-technical but critical KSAs for cybersecurity professional development has proved to be a factor in accelerated hiring for program participants.
The Impact of a GenCyber Camp on In-Service Teachers&apos; TPACK
Author: Thomas, Kevin M.
Date: 2022-00-00
Type: Journal Articles
Subjects: Technological Literacy,Pedagogical Content Knowledge,Information Security,Computer Security,Faculty Development,Camps,Computer Science Education,Teaching Methods,Middle School Teachers,High School Teachers,Teacher Attitudes,Self Evaluation (Individuals),Curriculum Development,Scores

The purpose of this study was to examine the impact of a GenCyber camp curriculum on teachers' technology, pedagogy, and content knowledge (TPACK). The camp was designed to engage participants in developing the knowledge and skills to incorporate GenCyber Cybersecurity First Principles and GenCyber Cybersecurity Concepts (GenCyber, 2019) into their curriculums. Participants (37 middle and high school teachers from a variety of disciplines) attended one of two weeklong camps held at a Midwestern liberal arts university. Using the TPACK Self-Reflection and TPACK Self-Assessment Surveys, pre- and post-camp data were collected from participants. Findings indicate that participants demonstrated an increase in all domains of the TPACK framework from pre- to post-survey. The greatest increase was in Technological Pedagogy Knowledge (TPK) (0.57), followed by Pedagogical Content Knowledge (PCK) (0.51), and Technological Pedagogical Content Knowledge (TPACK) (0.46). GenCyber participants also demonstrated an average increase in pre- and post-test scores in all areas on the TPACK Self-Assessment Survey Results; however, individual results were mixed. The majority of participants (n=21), sixty percent, saw an increase in composite score from pre- to post, whereas 12 participants' (34%) scores decreased from pre- to post, and two (6%) stayed the same. Findings indicate the GenCyber Camp provided in-service teachers with the knowledge and skills necessary to incorporate GenCyber Principles and Cybersecurity Concepts into their curriculum. Recommendations for teacher professional development on cybersecurity are made.
Amplifying Student and Administrator Perspectives on Equity and Bias in Learning Analytics: Alone Together in Higher Education
Author: Heiser, Rebecca E.
Date: 2023-00-00
Type: Journal Articles
Subjects: Student Attitudes,Administrator Attitudes,Equal Education,Bias,Higher Education,Inclusion,Stakeholders,Learning Analytics,Information Literacy,Professional Development,College Students,Diversity

When higher education institutions (HEIs) have the potential to collect large amounts of learner data, it is important to consider the spectrum of stakeholders involved with and impacted by the use of learning analytics. This qualitative research study aims to understand the degree of concern with issues of bias and equity in the uses of learner data as perceived by students, diversity and inclusion leaders, and senior administrative leaders in HEIs. An interview study was designed to investigate stakeholder voices that generate, collect, and utilize learning analytics from eight HEIs in the United States. A phased inductive coding analysis revealed similarities and differences in the three stakeholder groups regarding concerns about bias and equity in the uses of learner data. The study findings suggest that stakeholders have varying degrees of data literacy, thus creating conditions of inequality and bias in learning data. By centring the values of these critical stakeholder groups and acknowledging that intersections and hierarchies of power are critical to authentic inclusion, this study provides additional insight into proactive measures that institutions could take to improve equity, transparency, and accountability in their responsible learning analytics efforts.
Applying a Responsible Innovation Framework in Developing an Equitable Early Alert System: A Case Study
Author: Patterson, Chris R.
Date: 2023-00-00
Type: Journal Articles
Subjects: Learning Analytics,Innovation,School Holding Power,At Risk Students,Identification,Intervention,College Students,Ethics,Usability,Accountability,Student Empowerment

The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and implementation of data analytics in higher education. In a case study of creating an early-alert retention system at James Madison University, a working group of ~20 faculty, staff, and students creatively utilized the AIRR framework. The present study discusses how the AIRR framework was utilized to observe and enhance group processes, and the outcomes of those enhanced processes.
&quot;It&apos;s like a Double-Edged Sword&quot;: Mentor Perspectives on Ethics and Responsibility in a Learning Analytics-Supported Virtual Mentoring Program
Author: Lee, Hakeoung Hannah
Date: 2023-00-00
Type: Journal Articles
Subjects: Learning Analytics,Mentors,Ethics,Responsibility,Electronic Learning,Online Courses,Equal Education,Undergraduate Students,Secondary School Students,Affordances,Barriers,Interpersonal Relationship

Data-driven learning analytics (LA) exploits artificial intelligence, data-mining, and emerging technologies, rapidly expanding the collection and uses of learner data. Considerations of potential harm and ethical implications have not kept pace, raising concerns about ethical and privacy issues (Holstein &amp; Doroudi, 2019; Prinsloo &amp; Slade, 2018). This empirical study contributes to a growing critical conversation on fairness, equity, and responsibility of LA lending mentor voices in the context of an online mentorship program through which undergraduate students mentored secondary school students. Specifically, this study responds to a phenomenon shared by four mentors who recounted hiding from mentees that they had seen their LA data. Interviews reveal the convergent and divergent ideas of mentors regarding LA in terms of: (1) affordances and constraints; (2) scope and boundaries; (3) ethical tensions and dilemmas; (4) paradoxical demands; and (5) what constitutes fairness, equity, and responsibility. The analysis integrates mentor voices with Slade and Prinsloo's (2013) principles for an ethical framework for LA, Hacking's (1982, 1986) dynamic nominalism, and Levinas's (1989) ethics of responsibility. Design recommendations derived from mentor insights are extended in a discussion of ethical relationality, troubling learners as data-subjects, and considering the possibilities of the agency, transparency, and choice in LA system design.
Learning Analytics and the Abolitionist Imagination
Author: Swauger, Shea
Date: 2023-00-00
Type: Journal Articles
Subjects: Learning Analytics,Justice,Imagination,Futures (of Society),Vignettes,Power Structure,Design,Equal Education,Accountability,Student Centered Learning,College Students

This article advances an abolitionist reframing of learning analytics (LA) that explores the benefits of productive disorientation, considers potential harms and care made possible by LA, and suggests the abolitionist imagination as an important educational practice. By applying abolitionist concepts to LA, we propose it may be feasible to open new critiques and social futures that build toward equity-oriented LA design and implementation. We introduce speculative methods to advance three vignettes imagining how LA could be weaponized against students or transformed into a justice-directed learning tool. Our speculative methods aim to destabilize where power in LA has been routinely located and contested, thereby opening new lines of inquiry about more equitable educational prospects. Our concluding discussion addresses how speculative design and fiction are complementary methods to the abolitionist imagination and can be pragmatic tools to help build a world with fairer, more equitable, and responsible LA technologies.
Learning Analytics in Physics Education: Equity-Focused Decision-Making Lacks Guidance!
Author: Grimm, Adrian
Date: 2023-00-00
Type: Journal Articles
Subjects: Physics,Science Instruction,Learning Analytics,Equal Education,Guidelines,Vignettes,Theory Practice Relationship,Evaluation Criteria,Standards,Diversity,Ethics,Learning Trajectories,Educational History,Gender Differences,Foreign Countries,Race,Social Class,Intersectionality,Bias

Learning Analytics are an academic field with promising usage scenarios for many educational domains. At the same time, learning analytics come with threats such as the amplification of historically grown inequalities. A range of general guidelines for more equity-focused learning analytics have been proposed but fail to provide sufficiently clear guidance for practitioners. With this paper, we attempt to address this theory-practice gap through domain-specific (physics education) refinement of the general guidelines We propose a process as a starting point for this domain-specific refinement that can be applied to other domains as well. Our point of departure is a domain-specific analysis of historically grown inequalities in order to identify the most relevant diversity categories and evaluation criteria. Through two focal points for normative decision-making, namely equity and bias, we analyze two edge cases and highlight where domain-specific refinement of general guidance is necessary. Our synthesis reveals a necessity to work towards domain-specific standards and regulations for bias analyses and to develop counter-measures against (intersectional) discrimination. Ultimately, this should lead to a stronger equity-focused practice in future.
New Vistas on Responsible Learning Analytics: A Data Feminist Perspective
Author: Pargman, Teresa Cerratto
Date: 2023-00-00
Type: Journal Articles
Subjects: Learning Analytics,Responsibility,Feminism,Ethics,Guidelines,Critical Theory,Data Science,Algorithms,Decision Making,Power Structure

The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners a valuable lens through which to consider LA as a moral practice. This paper examines what data feminism can offer the LA community. It identifies critical questions for further developing and enabling a responsible stance in LA research and practice taking one particular case -- algorithmic decision-making -- as a point of departure.
Serious Game Analytics by Design: Feature Generation and Selection Using Game Telemetry and Game Metrics--Toward Predictive Model Construction
Author: Lu, Wenyi
Date: 2023-00-00
Type: Journal Articles
Subjects: Educational Games,Learning Analytics,Design,Game Based Learning,Performance Based Assessment,Predictive Measurement,Middle School Students,Science Instruction,Water,Computer Simulation,Information Science

The construction of prediction models reflecting players' learning performance in serious games currently faces various challenges for learning analytics. In this study, we design, implement, and field test a learning analytics system for a serious game, advancing the field by explicitly showing which in-game features correspond to differences in learner performance. We then deploy and test a system that provides instructors with clear signals regarding student learning and progress in the game, which instructors could depend upon for interventions. Within the study, we examined, coded, and filtered a substantial gameplay corpus, determining expertise in the game. Mission HydroSci (MHS) is a serious game that teaches middle-school students water science. Using our logging system, designed and implemented along with game design and development, we captured around 60 in-game features from the gameplay of 373 students who completed Unit 3 of MHS in its first field test. We tested eight hypotheses during the field test and presented this paper's results to participating teachers. Our findings reveal several features with statistical significance that will be critical for creating a validated prediction model. We discuss how this work will help future research establish a framework for designing analytics systems for serious games and advancing gaming design and analytics theory.
The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation
Author: Conijn, Rianne
Date: 2023-00-00
Type: Journal Articles
Subjects: Ethics,Writing Evaluation,Artificial Intelligence,Essays,Trust (Psychology),Scoring,Student Motivation,Guidelines,Comparative Analysis,Algorithms,Accuracy,Automation,Undergraduate Students,Student Attitudes,Academic Language,Computer Software,Foreign Countries

Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The current study aimed to identify the effect of explanations of an automated essay scoring system on students' trust and motivation. The explanations were designed using a needs-elicitation study with students in combination with guidelines and frameworks of explainable AI. Two types of explanations were tested: full-text global explanations and an accuracy statement. The results showed that both explanations did not have an effect on student trust or motivation compared to no explanations. Interestingly, the grade provided by the system, and especially the difference between the student's self-estimated grade and the system grade, showed a large influence. Hence, it is important to consider the effects of the outcome of the system (here: grade) when considering the effect of explanations of AI in education.
Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education
Author: Rets, Irina
Date: 2023-00-00
Type: Journal Articles
Subjects: Ethics,Learning Analytics,Evidence Based Practice,Guidelines,Interdisciplinary Approach,Distance Education,College Students,Privacy,Predictive Measurement,Technology Uses in Education

The progressive move of higher education institutions (HEIs) towards blended and online environments, accelerated by COVID-19, and their access to a greater variety of student data has heightened the need for ethical learning analytics (LA). This need is particularly salient in light of a lack of comprehensive, evidence-based guidelines on ethics that address gaps voiced in LA ethics research. Studies on the topic are predominantly conceptual, representing mainly institutional rather than stakeholder views, with some areas of ethics remaining underexplored. In this paper, we address this need by using a case of four years of interdisciplinary research in developing the award-winning Early Alerts Indicators (EAI) dashboard at a distance learning university. Through a lens focused on ethical considerations and informed by the practical approach to ethics, we conducted a case study review, using 10 relevant publications that report on the development and implementation of the tool. Our six practical recommendations on how to ethically engage with LA can inform an ethical development of LA that not only protects student privacy, but also ensures that LA tools are used in ways that effectively support student learning and development.
The Promise of MOOCs Revisited? Demographics of Learners Preparing for University
Author: Meaney, Michael J.
Date: 2023-00-00
Type: Journal Articles
Subjects: MOOCs,Ethics,Equal Education,Socioeconomic Status,Disproportionate Representation,Barriers,College Attendance,Algorithms,Outcomes of Education,Access to Education,Learning Analytics,Distance Education,Comparative Analysis,Learner Engagement,Academic Achievement,Grades (Scholastic),Student Characteristics

This paper leverages cluster analysis to provide insight into how traditionally underrepresented learners engage with entry-level massive open online courses (MOOCs) intended to lower the barrier to university enrolment, produced by a major research university in the United States. From an initial sample of 260,239 learners, we cluster analyze a subset of data from 29,083 participants who submitted an assignment in one of nine entry-level MOOC courses. Manhattan distance and Gower distance measures are computed based on engagement, achievement, and demographic data. To our knowledge, this marks one of the first such uses of Gower distance to cluster mixed variable data to explore fairness and equity in the MOOC literature. The clusters are derived from CLARA and PAM algorithms, enriched by demographic data, with a particular focus on education level, as well as approximated socioeconomic status (SES) for a smaller subset of learners. Results indicate that learners without a college degree are more likely to be high-performing compared to college-educated learners. Learners from lower SES backgrounds are just as likely to be successful as learners from middle and higher SES backgrounds. While MOOCs have struggled to improve access to learning, more fair and equitable outcomes for traditionally underrepresented learners are possible.