Search

Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformation detection rely on time and resources consuming fine-tuning and complex model structures. With the outstanding performance of LLMs, many studies have employed them for misinformation detection. Unfortunately, they focus on in-domain tasks and do not incorporate significant sentiment and emotion features (which we jointly call affect). In this paper, we propose RAEmoLLM, the first retrieval augmented (RAG) LLMs framework to address cross-domain misinformation detection using in-context learning based on affective information. It accomplishes this by applying an emotion-aware LLM to construct a retrieval database of affective embeddings. This database is used by our retrieval module to obtain source-domain samples, which are subsequently used for the inference module's in-context few-shot learning to detect target domain misinformation. We evaluate our framework on three misinformation benchmarks. Results show that RAEmoLLM achieves significant improvements compared to the zero-shot method on three datasets, with the highest increases of 20.69%, 23.94%, and 39.11% respectively. This work will be released on https://github.com/lzw108/RAEmoLLM.

In cultural heritage conservation, it is increasingly common to rely on non-destructive imaging methods based on the absorption or scattering of photons ($X$ or $\gamma$ rays) or neutrons. However, physical and practical issues limit these techniques: their penetration depth may be insufficient for large and dense objects, they require transporting the objects of interest to dedicated laboratories, artificial radiation is hazardous and may induce activation in the material under study. Muons are elementary particles abundantly and freely produced in cosmic-ray interactions in the atmosphere. Their absorption and scattering in matter are characteristically dependent on the density and elemental composition of the material that they traverse, which offers the possibility of exploiting them for sub-surface remote imaging. This novel technique, nicknamed "muography", has been applied in use cases ranging from geophysics to archaeology to nuclear safety, but it has been so far under-explored for a vast category of cultural heritage objects that are relatively large (from decimeters to human size) and dense (stone, metals). The development of portable muon detectors makes muography particularly competitive in cases where the items to be analysed are not transportable, or set up in a confined environment. This document reviews the relevant literature, presents some exemplary use cases, and critically assesses the strengths and weaknesses of muography in this context., Comment: Submitted to a special issue of iScience about "Cosmic-Ray Muography and Applications"

We present the first shared task on Semantic Textual Relatedness (STR). While earlier shared tasks primarily focused on semantic similarity, we instead investigate the broader phenomenon of semantic relatedness across 14 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Punjabi, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia -- regions characterised by the relatively limited availability of NLP resources. Each instance in the datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. Participating systems were asked to rank sentence pairs by their closeness in meaning (i.e., their degree of semantic relatedness) in the 14 languages in three main tracks: (a) supervised, (b) unsupervised, and (c) crosslingual. The task attracted 163 participants. We received 70 submissions in total (across all tasks) from 51 different teams, and 38 system description papers. We report on the best-performing systems as well as the most common and the most effective approaches for the three different tracks., Comment: SemEval 2024 Task Description Paper. arXiv admin note: text overlap with arXiv:2402.08638

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present \textit{SemRel}, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: \textit{Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish,} and \textit{Telugu}. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia -- regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP., Comment: Accepted to the Findings of ACL 2024

Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges. However, as multi-agent systems grow in complexity, gaining a comprehensive understanding of their behaviour becomes increasingly challenging. Conventionally, tracking team rewards over time has served as a pragmatic measure to gauge the effectiveness of agents in learning optimal policies. Nevertheless, we argue that relying solely on the empirical returns may obscure crucial insights into agent behaviour. In this paper, we explore the application of explainable AI (XAI) tools to gain profound insights into agent behaviour. We employ these diagnostics tools within the context of Level-Based Foraging and Multi-Robot Warehouse environments and apply them to a diverse array of MARL algorithms. We demonstrate how our diagnostics can enhance the interpretability and explainability of MARL systems, providing a better understanding of agent behaviour., Comment: 4 pages, AAAI XAI4DRL workshop 2023

Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred from a single shared global reward. Arguably, among the best current approaches to effectively measure individual agent contributions is to use Shapley values. However, calculating these values is expensive as the computational complexity grows exponentially with respect to the number of agents. In this paper, we adapt difference rewards into an efficient method for quantifying the contribution of individual agents, referred to as Agent Importance, offering a linear computational complexity relative to the number of agents. We show empirically that the computed values are strongly correlated with the true Shapley values, as well as the true underlying individual agent rewards, used as the ground truth in environments where these are available. We demonstrate how Agent Importance can be used to help study MARL systems by diagnosing algorithmic failures discovered in prior MARL benchmarking work. Our analysis illustrates Agent Importance as a valuable explainability component for future MARL benchmarks., Comment: 8 pages, AAAI XAI4DRL workshop 2023; references updated, figure 8 style updated, typos

Establishing sound experimental standards and rigour is important in any growing field of research. Deep Multi-Agent Reinforcement Learning (MARL) is one such nascent field. Although exciting progress has been made, MARL has recently come under scrutiny for replicability issues and a lack of standardised evaluation methodology, specifically in the cooperative setting. Although protocols have been proposed to help alleviate the issue, it remains important to actively monitor the health of the field. In this work, we extend the database of evaluation methodology previously published by containing meta-data on MARL publications from top-rated conferences and compare the findings extracted from this updated database to the trends identified in their work. Our analysis shows that many of the worrying trends in performance reporting remain. This includes the omission of uncertainty quantification, not reporting all relevant evaluation details and a narrowing of algorithmic development classes. Promisingly, we do observe a trend towards more difficult scenarios in SMAC-v1, which if continued into SMAC-v2 will encourage novel algorithmic development. Our data indicate that replicability needs to be approached more proactively by the MARL community to ensure trust in the field as we move towards exciting new frontiers., Comment: 6 pages, AAAI XAI4DRL workshop 2023; typos corrected, images updated, page count updated

Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times. To address this difficulty, we propose the framework of Generalisable Agents for Neural Network Optimisation (GANNO) -- a multi-agent reinforcement learning (MARL) approach that learns to improve neural network optimisation by dynamically and responsively scheduling hyperparameters during training. GANNO utilises an agent per layer that observes localised network dynamics and accordingly takes actions to adjust these dynamics at a layerwise level to collectively improve global performance. In this paper, we use GANNO to control the layerwise learning rate and show that the framework can yield useful and responsive schedules that are competitive with handcrafted heuristics. Furthermore, GANNO is shown to perform robustly across a wide variety of unseen initial conditions, and can successfully generalise to harder problems than it was trained on. Our work presents an overview of the opportunities that this paradigm offers for training neural networks, along with key challenges that remain to be overcome., Comment: Accepted at the Workshop on Advanced Neural Network Training (WANT) and Optimization for Machine Learning (OPT) at NeurIPS 2023

Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms. In modern RL research, there is a need for simulated environments that are performant, scalable, and modular to enable their utilization in a wider range of potential real-world applications. Therefore, we present Jumanji, a suite of diverse RL environments specifically designed to be fast, flexible, and scalable. Jumanji provides a suite of environments focusing on combinatorial problems frequently encountered in industry, as well as challenging general decision-making tasks. By leveraging the efficiency of JAX and hardware accelerators like GPUs and TPUs, Jumanji enables rapid iteration of research ideas and large-scale experimentation, ultimately empowering more capable agents. Unlike existing RL environment suites, Jumanji is highly customizable, allowing users to tailor the initial state distribution and problem complexity to their needs. Furthermore, we provide actor-critic baselines for each environment, accompanied by preliminary findings on scaling and generalization scenarios. Jumanji aims to set a new standard for speed, adaptability, and scalability of RL environments., Comment: 9 pages + 21 pages of appendices and references. Published at ICLR 2024

Disagreements are frequently studied from the perspective of either detecting toxicity or analysing argument structure. We propose a framework of dispute tactics that unifies these two perspectives, as well as other dialogue acts which play a role in resolving disputes, such as asking questions and providing clarification. This framework includes a preferential ordering among rebuttal-type tactics, ranging from ad hominem attacks to refuting the central argument. Using this framework, we annotate 213 disagreements (3,865 utterances) from Wikipedia Talk pages. This allows us to investigate research questions around the tactics used in disagreements; for instance, we provide empirical validation of the approach to disagreement recommended by Wikipedia. We develop models for multilabel prediction of dispute tactics in an utterance, achieving the best performance with a transformer-based label powerset model. Adding an auxiliary task to incorporate the ordering of rebuttal tactics further yields a statistically significant increase. Finally, we show that these annotations can be used to provide useful additional signals to improve performance on the task of predicting escalation., Comment: Accepted to EMNLP 2022 (Long paper)

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving decentralised decision-making problems at scale. Research in the field has been growing steadily with many breakthrough algorithms proposed in recent years. In this work, we take a closer look at this rapid development with a focus on evaluation methodologies employed across a large body of research in cooperative MARL. By conducting a detailed meta-analysis of prior work, spanning 75 papers accepted for publication from 2016 to 2022, we bring to light worrying trends that put into question the true rate of progress. We further consider these trends in a wider context and take inspiration from single-agent RL literature on similar issues with recommendations that remain applicable to MARL. Combining these recommendations, with novel insights from our analysis, we propose a standardised performance evaluation protocol for cooperative MARL. We argue that such a standard protocol, if widely adopted, would greatly improve the validity and credibility of future research, make replication and reproducibility easier, as well as improve the ability of the field to accurately gauge the rate of progress over time by being able to make sound comparisons across different works. Finally, we release our meta-analysis data publicly on our project website for future research on evaluation: https://sites.google.com/view/marl-standard-protocol, Comment: Published at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Website: see https://sites.google.com/view/marl-standard-protocol . 43 Pages, 21 Figures, 8 Tables

Multi-agent reinforcement learning (MARL) research is inherently computationally expensive and it is often difficult to obtain a sufficient number of experiment samples to test hypotheses and make robust statistical claims. Furthermore, MARL algorithms are typically complex in their design and can be tricky to implement correctly. These aspects of MARL present a difficult challenge when it comes to creating useful software for advanced research. Our criteria for such software is that it should be simple enough to use to implement new ideas quickly, while at the same time be scalable and fast enough to test those ideas in a reasonable amount of time. In this preliminary technical report, we introduce Mava, a research library for MARL written purely in JAX, that aims to fulfill these criteria. We discuss the design and core features of Mava, and demonstrate its use and performance across a variety of environments. In particular, we show Mava's substantial speed advantage, with improvements of 10-100x compared to other popular MARL frameworks, while maintaining strong performance. This allows for researchers to test ideas in a few minutes instead of several hours. Finally, Mava forms part of an ecosystem of libraries that seamlessly integrate with each other to help facilitate advanced research in MARL. We hope Mava will benefit the community and help drive scientifically sound and statistically robust research in the field. The open-source repository for Mava is available at https://github.com/instadeepai/Mava.

Disagreements are pervasive in human communication. In this paper we investigate what makes disagreement constructive. To this end, we construct WikiDisputes, a corpus of 7 425 Wikipedia Talk page conversations that contain content disputes, and define the task of predicting whether disagreements will be escalated to mediation by a moderator. We evaluate feature-based models with linguistic markers from previous work, and demonstrate that their performance is improved by using features that capture changes in linguistic markers throughout the conversations, as opposed to averaged values. We develop a variety of neural models and show that taking into account the structure of the conversation improves predictive accuracy, exceeding that of feature-based models. We assess our best neural model in terms of both predictive accuracy and uncertainty by evaluating its behaviour when it is only exposed to the beginning of the conversation, finding that model accuracy improves and uncertainty reduces as models are exposed to more information., Comment: Accepted to appear in EACL 2021

To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs., Comment: 21 pages, 3 figures

While the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) are powerful tools for model selection in linear regression, they are built on different prior assumptions and thereby apply to different data generation scenarios. We show that in the finite-dimensional case their respective assumptions can be unified within an augmented model-plus-noise space and construct a prior in this space which inherits the beneficial properties of both AIC and BIC. This allows us to adapt the BIC to be robust against misspecified models where the signal to noise ratio is low., Comment: 17 pages, 4 figures

Motivated by the modeling of liquidity risk in fund management in a dynamic setting, we propose and investigate a class of time series models with generalized Pareto marginals: the autoregressive generalized Pareto process (ARGP), a modified ARGP (MARGP) and a thresholded ARGP (TARGP). These models are able to capture key data features apparent in fund liquidity data and reflect the underlying phenomena via easily interpreted, low-dimensional model parameters. We establish stationarity and ergodicity, provide a link to the class of shot-noise processes, and determine the associated interarrival distributions for exceedances. Moreover, we provide estimators for all relevant model parameters and establish consistency and asymptotic normality for all estimators (except the threshold parameter, which as usual must be dealt with separately). Finally, we illustrate our approach using real-world fund redemption data, and we discuss the goodness-of-fit of the estimated models.

A model-independent method for the analysis of the two-particle short-range correlations is presented, that can be utilized to describe e.g. Bose-Einstein (HBT), dynamical (ridge) or other correlation functions, that have a nearly L\'evy or streched exponential shape. For the special case of L\'evy exponent alpha = 1, the earlier Laguerre expansions are recovered, for the alpha = 2 special case, a new expansion method is obtained for nearly Gaussian correlation functions. Multi-dimensional L\'evy expansions are also introduced and their potential application to analyze rigde correlation data is discussed.

In the context of data modeling and comparisons between different fit models, Bayesian analysis calls that model best which has the largest evidence, the prior-weighted integral over model parameters of the likelihood function. Evidence calculations automatically take into account both the usual chi-squared measure and an Occam factor which quantifies the price for adding extra parameters. Applying Bayesian analysis to projections onto azimuth of 2D angular correlations from 200 GeV AuAu collisions, we consider typical model choices including Fourier series and a Gaussian plus combinations of individual cosine components. We find that models including a Gaussian component are consistently preferred over pure Fourier-series parametrizations, sometimes strongly so. For 0-5% central collisions the Gaussian-plus-dipole model performs better than Fourier Series models or any other combination of Gaussian-plus-multipoles., Comment: 6 pages, 4 figures. Proceedings of the XLV International Symposium on Multiparticle Dynamics, Wildbad Kreuth, 5-9 October 2015

Analysis and interpretation of spectrum and correlation data from high-energy nuclear collisions is currently controversial because two opposing physics narratives derive contradictory implications from the same data---one narrative claiming collision dynamics is dominated by dijet production and projectile-nucleon fragmentation, the other claiming collision dynamics is dominated by a dense, flowing QCD medium. Opposing interpretations seem to be supported by alternative data models, and current model-comparison schemes are unable to distinguish between them. There is clearly need for a convincing new methodology to break the deadlock. In this study we introduce Bayesian Inference (BI) methods applied to angular correlation data as a basis to evaluate competing data models. For simplicity the data considered are projections of 2D angular correlations onto 1D azimuth from three centrality classes of 200 GeV AuAu collisions. We consider several data models typical of current model choices, including Fourier series (FS) and a Gaussian plus various combinations of individual cosine components. We evaluate model performance with BI methods and with power-spectrum (PS) analysis. We find that the FS-only model is rejected in all cases by Bayesian analysis which always prefers a Gaussian. A cylindrical quadrupole cos(2\phi) is required in some cases but rejected for 0-5%-central AuAu collisions. Given a Gaussian centered at the azimuth origin "higher harmonics" cos(m\phi) for m > 2 are rejected. A model consisting of Gaussian + dipole cos(\phi) + quadrupole cos(2\phi) provides good 1D data descriptions in all cases., Comment: 24 pages, 24 figures

We propose that Bayesian variable selection for linear parametrisations with Gaussian iid likelihoods be based on the spherical symmetry of the diagonalised parameter space. Our r-prior results in closed forms for the evidence for four examples, including the hyper-g prior and the Zellner-Siow prior, which are shown to be special cases. Scenarios of a single variable dispersion parameter and of fixed dispersion are studied, and asymptotic forms comparable to the traditional information criteria are derived. A simulation exercise shows that model comparison based on our r-prior gives good results comparable to or better than current model comparison schemes., Comment: 14 pages, 4 figures

The usual chi-squared method of fit quality assessment is a special case of the more general method of Bayesian model comparison which involves integrals of the likelihood and prior over all possible values of all parameters. We introduce new parametrisations based on systematic expansions around the stretched exponential or Fourier-transformed L\'evy source distribution, and utilise the increased discriminating power of the Bayesian approach to evaluate the relative probability of these models to be true representations of a recently measured Bose-Einstein correlation data in e+e- annihilations at LEP., Comment: Proceedings of a talk of Michiel De Kock at the WPCF 2011 conference

Lowest-order cumulants provide important information on the shape of the emission source in femtoscopy. For the simple case of noninteracting identical particles, we show how the fourth-order source cumulant can be determined from measured cumulants in momentum space. The textbook Gram-Charlier series is found to be highly inaccurate, while the related Edgeworth series provides increasingly accurate estimates. Ordering of terms compatible with the Central Limit Theorem appears to play a crucial role even for nongaussian distributions., Comment: 11 pages, 2 figures

We describe briefly the properties of the recently completed Southern African Large Telescope (SALT), along with its first light imager SALTICAM. Using this instrument, we present 4.3 hr of high speed unfiltered photometric observations of the eclipsing polar SDSSJ015543.40+002807.2 with time resolution as short as 112 ms, the highest quality observations of this kind of any polar to date. The system was observed during its high luminosity state. Two accreting poles are clearly seen in the eclipse light curve. The binary system parameters have been constrained: the white dwarf mass is at the low end of the range expected for cataclysmic variables. Correlations between the positions of the accretion regions on or near the surface of the white dwarf and the binary system parameters were established. The sizes of the accretion regions and their relative movement from eclipse to eclipse were estimated: they are typically 4-7 deg depending on the mass of the white dwarf. The potential of these observations will only fully be realised when low state data of the same kind are obtained and the contact phases of the eclipse of the white dwarf are measured., Comment: 14 pages, 7 figures

This collection of nine papers is intended for policy makers and educators and marks the beginning of new emphasis on and debate about the indigenization of social and community work education in South Africa. "'New Careers for the Poor': A Review of the Career of an Innovative Idea" (Joseph Katan) reviews and examines the history of the idea of employing indigenous workers in human service organizations and the factors that affected its course of development. It traces the beginning of the idea of idigenization from the 1960s to present day concerns. "The Need for the Indigenisation of the Education of Social Workers in South Africa" (Thobeka T. Kuse) proposes that social work training incorporate the values of the people if it is to be effective in terms of service delivery."The Family Services Associate Curriculum: Social Work Education for Indigenous Workers" (Wilburn Hayden, Jr.) describes the curriculum. "Southern African Regional Approaches to Indigenising Social Work Education and Practice: The Case of Botswana" (Kwaku Osei-Hwedie) focuses on the seeming inability of programs to indigenize and the factors leading to this situation. "Indigenisation of Social Work Education: A Personal View at a University" (Jean D. Triegaardt) explores and analyzes those issues that have an impact on the process of indigenization from a university point of view. "Indigenisation of Social and Community Work Education: A Personal Technikon View" (Desiree de Kock) looks at the important role technikons (technical schools) have to play. "South African Perspectives of Indigenisation: A Personal Technikon View" (J. N. Steyn) concentrates on a technikon approach to the offering of courses. "The South African Curriculum and Qualification Requirements for Social Workers" (Jouberto Lombard) focuses on an overview of the origin and development of curricula and qualification requirements, explains the present curriculum and qualification requirements, and discusses a personal vision for them. "The UK (United Kingdom) Approach to Social Work Education and Qualification: The Diploma in Social Work" (Tony Hall) is partly a description of the new approach to social work and social care training and partly a case study in institutional change. Individual chapters contain references. (YLB)

Within the framework of the relativistic plane wave impulse approximation (RPWIA), complete sets of quasielastic $(\vec{p}, \vec{p}^{'})$ and $(\vec{p}, \vec{n})$ spin observables are calculated employing a general Lorentz invariant representation of the NN scattering matrix (referred to as the IA2 representation). The use of a complete representation eliminates the arbitrariness of a previously-used five-term parameterization (commonly called the IA1 representation) and allows for the correct incorporation of effective-mass-type medium effects within the RPWIA framework and within the context of the Walecka model. For quasielastic scattering from a $^{40}$Ca target at incident proton energies between 200 and 500 MeV, we investigate the sensitivity of complete sets of spin observables to effective nucleon masses for both IA1 and IA2 representations. In general it is seen that the IA1 representation may overestimate the importance of nuclear medium effects, whereas the IA2-based predictions nearly correspond to values for free nucleon-nucleon scattering., Comment: 16 pages, 4 figures, REVTeX. Submitted to Physical Review C

For the calculation of polarization transfer observables for quasielastic scattering of protons on nuclei, a formalism in the context of the Relativistic Plane Wave Impulse Approximation is developed, in which the interaction matrix is expanded in terms of a complete set of 44 independent invariant amplitudes. A boson-exchange model is used to predict the 39 amplitudes which were omitted in the formerly used five-term parameterization(the SPVAT form) of the nucleon-nucleon scattering matrix. Use of the complete set of amplitudes eliminates the arbitrariness of the five-term representation., Comment: 29 pages, 2 figures

Within the relativistic PWIA, spin observables have been recalculated for quasielastic ($\vec p,\vec p '$) and ($\vec p,\vec n$) reactions on a $^{40}$Ca target. The incident proton energy ranges from 135 to 300 MeV while the transferred momentum is kept fixed at 1.97 fm^{-1}. In the present calculations, new Horowitz-Love--Franey relativistic NN amplitudes have been generated in order to yield improved and more quantitative spin observable values than before. The sensitivities of the various spin observables to the NN interaction parameters, such as (1) the presence of the surrounding nuclear medium, (2) a pseudoscalar versus a pseudovector interaction term, and (3) exchange effects, point to spin observables which should preferably be measured at certain laboratory proton energies, in order to test current nuclear models. This study also shows that nuclear medium effects become more important at lower proton energies ($\leq$ 200 MeV). A comparison to the limited available data indicates that the relativistic parametrization of the NN scattering amplitudes in terms of only the five Fermi invariants (the SVPAT form) is questionable., Comment: 10 pages, 6 Postscript figures, uses psfig.sty and article.sty, submitted to Phys. Rev. C

This study was designed to assess the use of an individualized laboratory-oriented high school physics program used in one Black Hawk County, Iowa, school. The three problems investigated were: (1) what short-term and long-term learning resulted from the use of the materials, (2) what student characteristics were related to learning outcomes, and (3) what characteristics of the materials were related to learning outcomes? Data were collected on 48 students and involved general intelligence; sex; IOWA TESTS OF EDUCATIONAL DEVELOPMENT scores on the BACKGROUND IN THE NATURAL SCIENCES TEST, READING IN THE NATURAL SCIENCE TEST, QUANTITATIVE THINKING TEST and the Composite score; personality types as determined by the MYERS-BRIGGS TYPE INDICATOR; score on selected PSSC test items; scores on Part, unit and final tests constructed to evaluate understanding of concepts found in the materials; and rate of completion. Concepts in the areas of mechanics and wage motion were emphasized in the physics materials, with the processes of science being an integral part of the materials. Data analyses revealed that individual personality types showed no consistency in correlating significantly with program test scores or rate of progress; the sample was scholastically inclined when described by scores on the Iowa tests; the more able students achieved greater success with the materials than did the less able. (Author/PEB)

The article focuses on the student fieldtrips under the leadership of Dr Herman van Niekerk of University of Johannesburg. It mentions that students had the opportunity to explore a large selection of depositional environments, facies, fossils, and sedimentary processes. It also mentions that the trip had to be shortened and changed because of the COVID-19 pandemic.