Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

Learning processes are impacted by individuals’ capabilities (e.g., working memory capacity). Nowadays, the technological improvements like those promised by deep learning methods open new opportunities to adapt learning environments. However, studies using the abilities of these models to individualize learning remain currently rare. The current research project aims to tackle one of the major constraints imposed by video learning related to the transitory aspect of delivered information. The purpose is to individualize the learning, based on learners working memory capacity (WMC). To this end, several elements need to be taken into account: (1) How to automatically and efficiently evaluate WMC without impacting learning; (2) Analyze in details the actual effect of WMC on video learning; (3) Get the most efficient and acceptable solutions to reduce the effects of WMC.

During class, taking notes helps the encoding process, especially when students reformulate what is presented. After class, notes can be used for revision. Previous studies, mostly conducted in laboratory contexts, showed that note-taking on paper seems to be more efficient than on computer, probably because students are more likely to reformulate on paper. The originality of this study is to investigate students’ perceptions and strategies depending on their note-taking method during but also after class. Data of 108 students showed that students on paper (30%) declare making more reformulations and less multitasking that students on computer (70%). After class, review sheets are written for a large majority of the students on paper, whatever the method they used during class. These results suggest that even if computer is the most used method during class, paper is still used after class, and with paper are associated better strategies.

Scales are widely used to evaluate subjective dimensions in questionnaires. Two main formats are used: Likert scalesand Visual Analogue Scales (VAS). Previous studies have shown mixed results regarding which format to favor. The aimof the current study is to compare formats and presentation types for each type of scale. 658 participants participated inthe study and completed a trust scale. Several characteristics of scales (e.g., valence of anchors) were explored, and 11formats of scales were compared. The results show that participants’ responses were different according to the type ofscale (i.e., Likert or VAS), the initial cursor’s position in the VAS, and the anchors’ valence in the VAS. Differences interms of reliability were found between VAS formats and the number of categories in Likert scales. These findings suggestthat the scale format is crucial and may influence data collection as well as suggesting related conclusions.

Artificial Intelligence (AI) is an increasingly prevalent field that influences a number of other areas. It generates a multitudeof reactions among the general population, particularly anxiety, which impacts on the development, deployment, andregulation of AI. Nevertheless, experimental data on public perceptions toward AI are generally lacking. To fill thisgap, this paper presents a large-scale experiment conducted on the influence of the terms used to describe AI on peoplesperception. In a preliminary study (705 participants), words related to AI were extracted. In a second experiment (552participants), the impact of these terminologies on anxiety toward AI was explored. An unprecedented effect of wordingand a positive bias of perception toward computers was revealed by these experiments, compared to robots and newtechnologies. This research paves the way for future studies on the effect of words on perception in the field of AI andnew technologies.

Thanks to research breakthroughs, Artificial Intelligence (AI) has gained popularity in recent years. Nevertheless, itsacceptability by general public is a poorly researched subject. The unified theory of acceptance and use of technology(UTAUT), a popular model to evaluate acceptability, is generally used for technological products. The purpose of thisstudy is to ensure that UTAUT is an appropriated model to evaluate AI acceptability. In this paper, 705 participants wereinvited to evaluate the acceptability of tools that integrate AI at work in 2030. Relying on UTAUT, performance expectancy,effort expectancy, social influence, facilitating conditions, and intention to use were evaluated. Structural modeling sug-gests a significant influence of performance expectancy, social influence and facilitating conditions on intention to use AI(explained variance of 81%). This research paves the way for prospective research on the overall acceptability of AI.

Nitrogen (N) fertilizer recommendations for corn (Zea mays L.) in the US Midwest have been a puzzle for several decades, without agreement among stakeholders for which methodology is the best to balance environmental and economic outcomes. Part of the reason is the lack of long-term data of crop responses to N over multiple fields since trial data is often limited in the number of soils and years it can explore. To overcome this limitation, we designed an analytical platform based on crop simulations run over millions of farming scenarios over extensive geographies. The database was calibrated and validated using data from more than four hundred trials in the region. This dataset can have an important role for research and education in N management, machine leaching, and environmental policy analysis. The calibration and validation procedure provides a framework for future gridded crop model studies. We describe dataset characteristics and provide thorough descriptions of the model setup.

In area and depth, the Pan-STARRS1 (PS1) 3π survey is unique among many-epoch, multi-band surveys and has enormous potential for the all-sky identification of variable sources. PS1 has observed the sky typically seven times in each of its five bands (grizy) over 3.5 years, but unlike SDSS, not simultaneously across the bands. Here we develop a new approach for quantifying statistical properties of non-simultaneous, sparse, multi-color light curves through light curve structure functions, effectively turning PS1 into a ∼35-epoch survey. We use this approach to estimate variability amplitudes and timescales (ωr, τ) for all point sources brighter than rP1 = 21.5 mag in the survey. With PS1 data on SDSS Stripe 82 as "ground truth," we use a Random Forest Classifier to identify QSOs and RR Lyrae based on their variability and their mean PS1 and WISE colors. We find that, aside from the Galactic plane, QSO and RR Lyrae samples of purity ∼75% and completeness ∼92% can be selected. On this basis we have identified a sample of ∼1,000,000 QSO candidates, as well as an unprecedentedly large and deep sample of ∼150,000 RR Lyrae candidates with distances from ∼10 to ∼120 kpc. Within the Draco dwarf spheroidal, we demonstrate a distance precision of 6% for RR Lyrae candidates. We provide a catalog of all likely variable point sources and likely QSOs in PS1, a total of 25.8 × 106 sources.

We use deep narrowband CaHK (F395N) imaging taken with the Hubble Space Telescope (HST) to construct the metallicity distribution function (MDF) of Local Group ultra-faint dwarf galaxy Eridanus II (Eri II). When combined with archival F475W and F814W data, we measure metallicities for 60 resolved red giant branch stars as faint as m F475W ∼ 24 mag, a factor of ∼4 more stars than current spectroscopic MDF determinations. We find that Eri II has a mean metallicity of [Fe/H] = -2.50 and a dispersion of, which are consistent with spectroscopic MDFs, though more precisely constrained owing to a larger sample. We identify a handful of extremely metal-poor star candidates (EMP; [Fe/H] < -3) that are marginally bright enough for spectroscopic follow-up. The MDF of Eri II appears well described by a leaky box chemical evolution model. We also compute an updated orbital history for Eri II using Gaia eDR3 proper motions, and find that it is likely on first infall into the Milky Way. Our findings suggest that Eri II underwent an evolutionary history similar to that of an isolated galaxy. Compared to MDFs for select cosmological simulations of similar mass galaxies, we find that Eri II has a lower fraction of stars with [Fe/H] < -3, though such comparisons should currently be treated with caution due to a paucity of simulations, selection effects, and known limitations of CaHK for EMPs. This study demonstrates the power of deep HST CaHK imaging for measuring the MDFs of UFDs.

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) 3π survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of RR Lyrae stars in this data set poses a challenge, given PS1's sparse, asynchronous multi-band light curves (≲12 epochs in each of five bands, taken over a 4.5 year period). We present a novel template fitting technique that uses well-defined and physically motivated multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate period estimates, precise to 2 s in >80% of cases. We augment these light-curve fits with other features from photometric time-series and provide them to progressively more detailed machinelearned classification models. From these models, we are able to select the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR Lyrae stars to date. The PS1 sample of ∼45,000 RRab stars is pure (90%) and complete (80% at 80 kpc) at high galactic latitudes. It also provides distances that are precise to 3%, measured with newly derived period luminosity relations for optical/near-infrared PS1 bands. With the addition of proper motions from Gaia and radial velocity measurements from multi-object spectroscopic surveys, we expect the PS1 sample of RR Lyrae stars to become the premier source for studying the structure, kinematics, and the gravitational potential of the Galactic halo. The techniques presented in this study should translate well to other sparse, multi-band data sets, such as those produced by the Dark Energy Survey and the upcoming Large Synoptic Survey Telescope Galactic plane sub-survey.