A Study of Homophobia Among HBCU Undergraduate Students Toward Gay and Lesbian Adoption

While recent research examines homophobia among social work majors, there is limited research devoted to social work students enrolled in a historically Black college and university (HBCU). Social workers are not immune to possessing homophobic attitudes; therefore, diversity training is important. This research explored whether there were any differences in the attitudes of undergraduate social work students enrolled in a HBCU in the South toward gay and lesbian adoption in relation to the students’ gender, age, religious affiliation, and religious participation. This quantitative study used a nonrandom convenience sample of 94 students using the Attitudes Toward Lesbians and Gay Men Scale and the Attitudes Toward Homosexuality Scale. The data were analyzed utilizing one-way and two-way chi-square procedures.     

USING ITEM RESPONSE THEORY TO CONDUCT A DISTRACTER ANALYSIS ON CONCEPTUAL INVENTORY OF NATURAL SELECTION

Concept inventories are often used to assess current student understanding although conceptual change models are problematic. Due to controversies with conceptual change models and the realities of student assessment, it is important that concept inventories are evaluated using a variety of theoretical models to improve quality. This study used a modified item response theory model to determine university nonmajor biology students’ levels of understanding of natural selection (n = 1,192). Using Conceptual Inventory of Natural Selection, we have reported how we applied Bock’s modified nominal item response theory model and the distracter test item analysis. We found that the use of this model can define student levels of understanding and identify problematic distracters.     

A walk down memory lane: on the relationship between autobiographical memories and outdoor activities

This article highlights a theoretical and practical framework for integrating the neuropsychological concept of autobiographical memory with the experiential learning that takes place in the outdoors. Autobiographical memories, our recollections of specific, personal events, are constructed through a personal narrative process; the way we choose to tell the stories of our lives, to ourselves, and crucially to others. Our interdisciplinary framework suggests that the participant comes to value memories of particular past events because they come to be seen as part of a shared history with others. Elements of outdoor activities can aid in the formulation of personal narratives and the generation of autobiographical memories. Our case study of a person with congenital deafblindness shows that taking an outdoor education approach can aid in providing shared authentic memorable experiences and in facilitating the construction of autobiographical memories in the bodily tactile modality.     

Interval running with self-selected recovery: Physiology,performance, and perception

This study (1) compared the physiological responses and performance during a high-intensity interval training (HIIT) session incorporating externally regulated (ER) and self-selected (SS) recovery periods and (2) examined the psychophysiological cues underpinning SS recovery durations. Following an incremental maximal exercise test to determine maximal aerobic speed (MAS), 14 recreationally active males completed 2 HIIT sessions on a non-motorised treadmill. Participants performed 12?×?30?s running intervals at a target intensity of 105% MAS interspersed with 30?s (ER) or SS recovery periods. During SS, participants were instructed to provide themselves with sufficient recovery to complete all 12 efforts at the required intensity. A semi-structured interview was undertaken following the completion of SS. Mean recovery duration was longer during SS (51?±?15?s) compared to ER (30?±?0?s; p?d?=?1.46?±?0.46). Between-interval heart rate recovery was higher (SS: 19?±?9?b?min^?1; ER: 8?±?5?b?min^?1; p?d?=?1.43?±?0.43) and absolute time ≥90% maximal heart rate (HR_max) was lower (SS: 335?±?193?s; ER: 433?±?147?s; p?=?.075; d?=?0.52?±?0.39) during SS compared to ER. Relative time ≥105% MAS was greater during SS (90?±?6%) compared to ER (74?±?20%; p?d?=?0.87?±?0.40). Different sources of afferent information underpinned decision-making during SS. The extended durations of recovery during SS resulted in a reduced time ≥90% HR_max but enhanced time ≥105% MAS, compared with ER exercise. Differences in the afferent cue utilisation of participants likely explain the large levels of inter-individual variability observed.     

Learning theories for artificial intelligence promoting learning processes

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic

• Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
• Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
• Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.

What this paper adds

• A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
• A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
• The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
• Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.

Implications for practice and policy

• Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
• Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

     

Assessing approaches to learning with nonparametric multidimensional scaling

This article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in Whyville, an online game environment with 8 million registered young learners. The learning objectives of two games included awareness and knowledge of NASA missions, developing knowledge and skills of measurement and scaling, applying measurement for planetary comparisons in the solar system. Trace data from 1361 interactions were analysed with nonparametric multidimensional scaling methods, which permitted visual examination and statistical validation, and provided an example and proof of concept for the multidimensional scaling approach to analysis of time-based behavioural data from a game or simulation. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth. The theoretical framework of the method and analysis makes use of the Epistemic Network Analysis toolkit as a post hoc data exploration platform, and the discussion centres on issues of semantic interpretation of interaction end-states and the application of evidence centred design in post hoc analysis.

Practitioner notes

What is already known about this topic

• Educational game play has been demonstrated to positively affect learning performance and learning persistence.
• Trace-based assessment from digital learning environments can focus on learning outcomes and processes drawn from user behaviour and contextual data.
• Existing approaches used in learning analytics do not (fully) meet criteria commonly used in psychometrics or for different forms of validity in assessment, even though some consider learning analytics a form of assessment in the broadest sense.
• Frameworks of knowledge representation in trace-based research often include concepts from cognitive psychology, education and cognitive science.

What this paper adds

• To assess skills-in-action, stronger connections of learning analytics with educational measurement can include parametric and nonparametric statistics integrated with theory-driven modelling and semantic network analysis approaches widening the basis for inferences, validity, meaning and understanding from digital traces.
• An expanded methodological foundation is offered for analysis in which nonparametric multidimensional scaling, multimodal analysis, epistemic network analysis and evidence-centred design are combined.

Implications for practice and policy

• The new foundations are suggested as a principled, theory-driven, embedded data collection and analysis framework that provides structure for reverse engineering of semantics as well as pre-planning frameworks that support creative freedom in the processes of creation of digital learning environments.

     

Early home learning support and home mathematics environment as predictors of children's mathematical skills between age 4 and 6: A longitudinal analysis using video observations and survey data

This large-scale and longitudinal study examines early home support for learning, formal/informal home mathematics activities, and their associations with children's mathematical development between age two and six. Data were collected in Germany between 2012 and 2018, N = 1184 (49% girls, 51% boys), and 15% of children had parents with a migration history. Linguistically and mathematically stimulating, attentive, and responsive parent–child engagement at age two predicted children's mathematical skills at age four and six (small-to-medium effect size). Both formal and informal home mathematical activities at age five predicted children's mathematical skills at age six (small effect size), and were associated with children's prior mathematics attainment. This study also provides indicators where individual differences and social circumstances are relevant to understanding different early mathematics outcomes.