Differential Learning Research Articles

Differential learning assisted with SANTUY mobile application for improving students’ mathematical understanding and ability

Equations are often used in mathematics and other subjects, which means that a media can teach students how to solve equations, particularly linear equations of two variables, or Sistem Persamaan Linear Dua Variabel (SPLDV) as it is called in Indonesian, is needed. However, most teachers are still focused on developing the learning process and teaching materials rather than developing an application technology-based learning media. As we know that technology-based learning is currently the focus of teaching and learning activities in the digital era, especially learning after the pandemic. This study aimed to develop an application that can be a reference for teaching media in learning activities to improve students’ mathematical understanding and ability. The developed application is named, Integrated and Convenient Android-based Simulation or Simulasi Android Terpadu dan Nyaman, abbreviated as SANTUY. This development study with Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model, explained in detail in the paper, took seventy-five subjects consisting of six expert validators, nine users, and sixty students. The expert validators comprised three media experts and three material experts. The evaluation stage involved students from two groups, each consisting of thirty students. The results showed that the average percentage of the feasibility of the SANTUY SPLDV solution simulation-assisted application with differential learning nuance was 79.62%, which means the application is adequate. The level of usefulness of the SANTUY SPLDV solution simulation-assisted application with differential learning nuance was 86.11%, which is categorized as “excellent”. In addition, using the SANTUY SPLDV application had a direct positive influence on improving students’ mathematical understanding and ability.

Differential learning methods for solving fully nonlinear PDEs

Differential learning methods for solving fully nonlinear PDEs

Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments.

Neuromodulation and differential learning across mosquito species.

Mosquitoes can change their feeding behaviours based on past experiences, such as shifting from biting animals to biting humans or avoiding defensive hosts (Wolff & Riffell 2018 J. Exp. Biol. 221, jeb157131. (doi:10.1242/jeb.157131)). Dopamine is a critical neuromodulator for insects, allowing flexibility in their feeding preferences, but its role in the primary olfactory centre, the antennal lobe (AL), remains unclear (Vinauger et al. 2018 Curr. Biol. 28, 333-344.e8. (doi:10.1016/j.cub.2017.12.015)). It is also unknown whether mosquitoes can learn some odours and not others, or whether different species learn the same odour cues. We assayed aversive olfactory learning in four mosquito species with different host preferences, and found that they differentially learn odours salient to their preferred host. Mosquitoes that prefer humans learned odours found in mammalian skin, but not a flower odour, and a nectar-feeding species only learned a floral odour. Comparing the brains of these four species revealed significantly different innervation patterns in the AL by dopaminergic neurons. Calcium imaging in the Aedes aegypti AL and three-dimensional image analyses of dopaminergic innervation show that glomeruli tuned to learnable odours have significantly higher dopaminergic innervation. Changes in dopamine expression in the insect AL may be an evolutionary mechanism to adapt olfactory learning circuitry without changing brain structure and confer to mosquitoes an ability to adapt to new hosts.

Intent to transfer learning amongst adult learners with differential learning orientations

In Singapore, the government has invested significant resources into its SkillsFuture training programmes, which were established with the goal of ensuring that the skills of workforce members remain current and continue to meet the demands of the global economy. To ensure that these initiatives yield the best outcomes, however, learners must actually transfer what they have learned to their workplaces post-completion. The present study drew upon data collected as part of a larger research programme that focused on the topic of adult learners’ motivations and intent to transfer in further learning programmes. In the present study, cluster analysis was used to identify whether adult learners in one polytechnic (n=444) fell into distinct ‘learning orientation’ profiles based on their learning motivation goals and levels of intrinsic and extrinsic motivation to learn. This analysis suggested three distinct learning orientation profile clusters (Idealists, Self-Actualists and Pragmatists), who differed significantly in terms of their learning motivation and intent to transfer. Other differences observed between these clusters (i.e., whether they received rewards for programme completion; whether they were given a choice about enrolling into the training programmes; in the level of support they received to attend the programmes; and the perceived relevance of the programmes to their own situations) also underscored potential ways in which the SkillsFuture initiative and associated further learning programmes could be enhanced to maximise their ultimate benefits for workplaces. Implications for policies and strategies to achieve this goal are discussed.

PIE-ARNet: Prior Image Enhanced Artifact Removal Network for Limited-Angle DECT

Dual-energy computed tomography (DECT) is of great clinical significance because it can simultaneously visualize the internal structure of the scanned object and provide material-specific information. DECT obtains two attenuation measurements of the same object at two different X-ray spectra, resulting in obvious redundant information. In this context, this article suggests acquiring dual-energy projection data using two complementary incomplete scans and utilizes a pretrained Prior-Net to generate the artifact-free prior image. Then the prior image is fed into the proposed prior image enhanced artifact removal network (PIE-ARNet) together with the degraded DECT images to improve the artifact removal performance. The generator of PIE-ARNet has two encoders and two decoders, with each component being responsible for a specific task. Two encoders extract and fuse prior information and image features, while two decoders perform differential learning for data in different energy channels. The discriminator of PIE-ARNet is dedicated to transferring the real statistical properties to the generated images, producing results with enhanced visual perception. Please note that Prior-Net could be trained using the freely available conventional single-energy CT data, which will not bring extra demand for DECT data. Experiments based on the simulated data and real rat data have demonstrated the promising performance of the proposed PIE-ARNet in removing artifacts, recovering image details, and preserving reconstruction accuracy.

TIME-Net: Transformer-Integrated Multi-Encoder Network for limited-angle artifact removal in dual-energy CBCT.

Dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique with foreseeable clinical applications. DE-CBCT images acquired with two different spectra can provide material-specific information. Meanwhile, the anatomical consistency and energy-domain correlation result in significant information redundancy, which could be exploited to improve image quality. In this context, this paper develops the Transformer-Integrated Multi-Encoder Network (TIME-Net) for DE-CBCT to remove the limited-angle artifacts. TIME-Net comprises three encoders (image encoder, prior encoder, and transformer encoder), two decoders (low- and high-energy decoders), and one feature fusion module. Three encoders extract various features for image restoration. The feature fusion module compresses these features into more compact shared features and feeds them to the decoders. Two decoders perform differential learning for DE-CBCT images. By design, TIME-Net could obtain high-quality DE-CBCT images using two complementary quarter-scans, holding great potential to reduce radiation dose and shorten the acquisition time. Qualitative and quantitative analyses based on simulated data and real rat data have demonstrated the promising performance of TIME-Net in artifact removal, subtle structure restoration, and reconstruction accuracy preservation. Two clinical applications, virtual non-contrast (VNC) imaging and iodine quantification, have proved the potential utility of the DE-CBCT images provided by TIME-Net.

Asymmetric Training in RealnessGAN

Generative adversarial networks (GANs) have demonstrated superior performances in image generation. In recent years, various improvements of network structure and learning theory related to GANs have undergone numerous advancement. Among these improvement techniques, the asymmetric training on the generator and discriminator networks has been widely adopted. For example, the batch normalization is used in generator while the spectral normalization is used in discriminator, or using different learning rates for the generator and discriminator. However, the asymmetric training on the real and generated samples has not been taken into consideration till now. In this paper, we proposed a novel asymmetric training-based RealnessGAN (ATRGAN) which applies the idea of asymmetric training on both samples and networks. Specifically, the asymmetric training on samples refers to performing the differential learning on the real and generated samples by controlling the information entropies of real and fake anchor distributions. The asymmetric training on networks is realized via the sampling transmission <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G2D$</tex-math></inline-formula> , which abandons the commonly used independent random sampling. With the help of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G2D$</tex-math></inline-formula> , the discriminator can obtain a dominant training position than the generator, so as to ensure that the discriminator can guide the generator more effectively during training. In addition, we proposed the floating anchor distribution technique and constructed the objective function of generator for ATRGAN. Through comparative experiments, we demonstrated ATRGAN's ability of achieving better generation performance than various SOTA GANs on CIFAR-10, CAT, and CelebA-HQ datasets.

Many Roads Lead to Rome: Differential Learning Processes for the Same Perceptual Improvement

Repeatedly exercising a perceptual ability usually leads to improvement, yet it is unclear whether the mechanisms supporting the same perceptual learning could be flexibly adjusted according to the training settings. Here, we trained adult observers in an orientation-discrimination task at either a single (focused) retinal location or multiple (distributed) retinal locations. We examined the observers’ discriminability (N = 52) and bias (N = 20) in orientation perception at the trained and untrained locations. The focused and distributed training enhanced orientation discriminability by the same amount and induced a bias in perceived orientation at the trained locations. Nevertheless, the distributed training promoted location generalization of both practice effects, whereas the focused training resulted in specificity. The two training tactics also differed in long-term retention of the training effects. Our results suggest that, depending on the training settings of the same task, the same discrimination learning could differentially engage location-specific and location-invariant representations of the learned stimulus feature.

The Impact of Differential Learning Model on Motor Skills and Handball-Specific Coordination Performance in 11–13-Year-Old Beginners in Handball

Aim: Improvement of skills in handball means an increase in performance. There are two main learning methods in sport: traditional (classical) and differential learning. The aim of our study was to determine the effect of plyometric training with differential learning method on agility and handball-specific coordination skills in 11-13-year-old handball athletes who are new to the sport.&#x0D; Methods: In this study, 44 male secondary school students aged 11-13 years from Istanbul and Adana provinces, who were new to sports, volunteered to take part in this study. Two groups of 22 students each were randomly selected as classical training group and differential learning group. All groups were given handball training for 4 weeks as adaptation training. The training sessions were conducted four days a week for 1.5 hours for 12 weeks. Pre-tests were applied before the training period started, and post-tests were applied at the end of 12 weeks. In the study, T-test, zigzag running, obstacle tests as motor skill agility tests; For handball coordination skills, handball slalom dribbling, wall quick pass, hand wall toss pass tests were applied.&#x0D; Results: Differential learning group (T-test, zig-zag running, obstacle, handball slalom dribbling, wall quick pass, hand wall toss pass tests) were statistically significant compared to the results of the classical training group (p

Learning Performance Styles in Gamified College Classes Using Data Clustering

This study aimed to investigate the efficacy of learning gamification in developing sustainable educational environments. To this end, gamified class data were analyzed to identify students’ learning performance patterns. The study sample comprised 369 data points collected across four point domains: Activity, Game, Project, and Exam Points, which students obtained in their gamified college courses conducted between 2016 and 2019. A K-means data clustering algorithm and silhouette analysis were utilized to evaluate student performances and determine differential learning styles in gamified environments. Cluster analysis revealed three types of learning patterns centered on performance, mastery, and avoidance. Based on our findings, we propose suggestions regarding class design for instructors considering using gamification strategies to support a sustainable educational environment. We also highlight the scope for future research in both in-person and online gamified learning.

Neural ODE Differential Learning and Its Application in Polar Motion Prediction

AbstractThis paper introduces a new learning algorithm for accurate, physically driven time series prediction. The fundamental assumption behind the method is that the phenomena follow Ordinary Differential Equations. We investigate the general case where the time series follows an ODE of degree . The resulting method is a learning algorithm based on the finite differences between the values of time series. We present the application of the method in the field of geodesy for polar motion prediction, the main objective of the present paper. We show that in this application, the linear form of the method is sufficient and offers competitive predictive performance. We present a baseline solution, in which we use historical polar motion time series from 1976 to predict up to the year 2020. The prediction horizon in this case is short‐term (up to 10 days into the future). In addition, we compare the prediction accuracy in the short‐term horizon with the results of the best performing model in the first Earth Orientation Prediction Comparison Campaign. On average, a 53% improvement in prediction performance is achieved. In further analyses, we compare the prediction accuracy for both short‐term and long‐term against the results of state‐of‐the‐art methods, namely Multichannel Singular Spectrum Analysis, and a combination of Singular Spectrum Analysis and Copula sampling. We show that the proposed method in this paper can outperform the mentioned two methods in both short and long‐term horizons, with an average improvement of the prediction performance of 54% and 52%, respectively.

Structural spine plasticity: Learning and forgetting of odor-specific subnetworks in the olfactory bulb.

Learning to discriminate between different sensory stimuli is essential for survival. In rodents, the olfactory bulb, which contributes to odor discrimination via pattern separation, exhibits extensive structural synaptic plasticity involving the formation and removal of synaptic spines, even in adult animals. The network connectivity resulting from this plasticity is still poorly understood. To gain insight into this connectivity we present here a computational model for the structural plasticity of the reciprocal synapses between the dominant population of excitatory principal neurons and inhibitory interneurons. It incorporates the observed modulation of spine stability by odor exposure. The model captures the striking experimental observation that the exposure to odors does not always enhance their discriminability: while training with similar odors enhanced their discriminability, training with dissimilar odors actually reduced the discriminability of the training stimuli. Strikingly, this differential learning does not require the activity-dependence of the spine stability and occurs also in a model with purely random spine dynamics in which the spine density is changed homogeneously, e.g., due to a global signal. However, the experimentally observed odor-specific reduction in the response of principal cells as a result of extended odor exposure and the concurrent disinhibition of a subset of principal cells arise only in the activity-dependent model. Moreover, this model predicts the experimentally testable recovery of odor response through weak but not through strong odor re-exposure and the forgetting of odors via exposure to interfering odors. Combined with the experimental observations, the computational model provides strong support for the prediction that odor exposure leads to the formation of odor-specific subnetworks in the olfactory bulb.

Wind speed and global radiation forecasting based on differential, deep and stochastic machine learning of patterns in 2-level historical meteo-quantity sets

AbstractAccurate forecasting of wind speed and solar radiation can help operators of wind farms and Photo-Voltaic (PV) plants prepare efficient and practicable production plans to balance the supply with demand in the generation and consumption of Renewable Energy (RE). Reliable Artificial Intelligence (AI) forecast models can minimize the effect of wind and solar power fluctuations, eliminating their intermittent character in system dispatch planning and utilization. Intelligent wind and solar energy management is essential in load scheduling and decision-making processes to meet user requirements. The proposed 24-h prediction schemes involve the beginning detection and secondary similarity re-evaluation of optimal day-data sequences, which is a notable incremental improvement against state-of-the-art in the consequent application of statistical AI learning. 2-level altitude measurements allow the identification of data relationships between two surface layers (hill and lowland) and adequate interpretation of various meteorological situations, whose differentiate information is used by AI models to recognize upcoming changes in the mid-term day horizon. Observations at two professional meteorological stations comprise specific quantities, of which the most valuable are automatically selected as input for the day model. Differential learning is a novel designed unconventional neurocomputing approach that combines derivative components produced in selected network nodes in the weighted modular output. The complexity of the node-stepwise composed model corresponds to the patterns included in the training data. It allows for representation of high uncertain and nonlinear dynamic systems, dependent on local RE production, not substantially reducing the input vector dimensionality leading to model over simplifications as standard AI does. Available angular and frequency time data (e.g., wind direction, humidity, and irradiation cycles) are combined with the amplitudes to solve reduced Partial Differential Equations (PDEs), defined in network nodes, in the periodical complex form. This is a substantial improvement over the previous publication design. The comparative results show better efficiency and reliability of differential learning in representing the modular uncertainty and PDE dynamics of patterns on a day horizon, taking into account recent deep and stochastic learning. A free available C++ parametric software together with the processed meteo-data sets allow additional comparisons with the presented model results.

Investigating the Effects of Differential Learning on Golfers' Pitching Performance as a Function of Handicap.

Traditionally, golf instruction has been oriented toward imitation of role models, guided by errors that surround a channel of supposedly correct repetition. Recent motor learning approaches relying on the dynamics of living systems suggest the inclusion of additional noise during practice for supporting players’ movement exploration and improving adaptability that in consequence will lead to increased performance. While the effectiveness of this approach has now been demonstrated in many sports, research exploring the effects of differential learning (DL) in golf is scarce, especially when considering different shot distances and players with various handicap levels. Therefore, the purpose of this study was to compare the effects of an enriched learning and information intervention as opposed to a more constrained approach, on the pitching performance of golfers with different handicaps from different distances. A total of 29 adolescent golfers with an average experience of 7.8 years were divided into a DL (n = 15) and a repetitive-oriented (RB, n = 14) group. Both groups were further compared dependent on their handicap level (DL, low handicap n = 7, high handicap n = 8; RB, low handicap n = 5, high handicap n = 9). The TrackMan 4 was used to measure the shot performance for 20 m, 35 m, and 50 m distances (10 shots from each distance) based on a pre- and post-test design. Each group performed the same number of trials (n = 270, 9 executions per distance per session) across 10 sessions. Analysis of covariance (ANCOVA) was used for the statistical analysis, using the pre-test as covariate and the post-test as dependent variable. The DL group revealed advantageous adaptations in the attack and face angle (p ≤ 0.05), while also in the dynamic loft (p ≤ 0.05), mostly for the 35 m and 50 m. In addition, this intervention led to improvements in the score, club head speed, and carry distance for the 50 m when compared to the RB (p ≤ 0.05; small effects). The low handicap players from the DL group also revealed adaptation in the angles’ variables (p ≤ 0.05) when compared with high handicap players, who improved the score (p ≤ 0.05) in all distances after intervention. The low handicap players from the RB group improved the score (p ≤ 0.05) and club speed (p ≤ 0.05) for the 20 and 35 m, while the high handicap golfers revealed higher improvements for these variables only in the 50 m distance condition. Overall, coaches may incorporate approaches into their skill training that increase the number of opportunities to improve the performance of both experienced and non-experienced players by promoting the adaptability of movement patterns.

Differential Repeated Sprinting Training in Youth Basketball Players: An Analysis of Effects According to Maturity Status.

The differential learning approach, which includes fluctuations that occur without movement repetitions and without corrections has received growing interest in the skill acquisition field. This study aimed to determine the effects of a 9-week training intervention involving differential repeated sprint training on a series of physical tests in youth basketball players. A total of 29 participants with different maturity statuses (pre-peak height velocity (PHV), n = 7; mid-PHV, n = 6; post-PHV, n = 16) completed 2 sessions per week of differential repeated sprint training for a period of 9 weeks. Sessions consisted of 2 × 10 repetitions sprints of 20-m whereby participants were instructed to perform various additional fluctuations for each repetition. Before and after the training intervention, participants completed jumping tests (countermovement jump (CMJ), single-leg CMJs, the modified 505 agility test, and straight sprinting tests (0–10 splits time), and maturity status was evaluated as well. Within-group analysis showed improvement in CMJ asymmetries and changes in direction asymmetries and 10-m sprint performance for the pre-, mid-, and post-PHV groups, respectively (p < 0.05), with large to very large effects. Analysis of covariance demonstrated that changes in sprint time in post-PHV players were greater than in the pre- and mid-PHV groups (p < 0.05), with moderate effect. Adding random fluctuations during repeated sprint training appear to be a suitable and feasible training strategy for maintaining and enhancing physical performance in youth basketball players, irrespective of maturity status. Furthermore, the present findings encourage practitioners to implement the present approach in youth athletes to improve their physical performance, but they should be aware that training response can vary according to maturity status.

Postlesson affordance‐based reflective discussion in ELT classes

AbstractThis article points out the need for postlesson affordance‐based reflective discussions as the traditional outcome‐based approach does not allow high levels of reflection to take place. After introducing the iceberg of reflection, it proposes a theoretical ground for discussions on the premise of learning affordances. Identifying the learning opportunities and factors of inhibition, teachers can holistically assess their teaching practices and orient the direction of future lessons. The article also suggests that postlesson affordance‐based discussions should channel more diverse stakeholders with teacher–student discussions, peer discussions, and self‐discussions based on the four lenses of critical reflection. Teachers can also invite different stakeholders in a joint discussion so that different voices can triangulate and complement each other. Acknowledging the importance of differential learning and teaching, the author recommends how teachers can hold discussions in different classroom contexts to foster language emergence and empower learners.

Sometimes I feel the fear of uncertainty: How intolerance of uncertainty and trait anxiety impact fear acquisition, extinction and the return of fear

It is hypothesized that the ability to discriminate between threat and safety is impaired in individuals with high dispositional negativity, resulting in maladaptive behavior. A large body of research investigated differential learning during fear conditioning and extinction protocols depending on individual differences in intolerance of uncertainty (IU) and trait anxiety (TA), two closely-related dimensions of dispositional negativity, with heterogenous results. These might be due to varying degrees of induced threat/safety uncertainty. Here, we compared two groups with high vs. low IU/TA during periods of low (instructed fear acquisition) and high levels of uncertainty (delayed non-instructed extinction training and reinstatement). Dependent variables comprised subjective (US expectancy, valence, arousal), psychophysiological (skin conductance response, SCR, and startle blink), and neural (fMRI BOLD) measures of threat responding. During fear acquisition, we found strong threat/safety discrimination for both groups. During early extinction (high uncertainty), the low IU/TA group showed an increased physiological response to the safety signal, resulting in a lack of CS discrimination. In contrast, the high IU/TA group showed strong initial threat/safety discrimination in physiology, lacking discriminative learning on startle, and reduced neural activation in regions linked to threat/safety processing throughout extinction training indicating sustained but non-adaptive and rigid responding. Similar neural patterns were found after the reinstatement test. Taken together, we provide evidence that high dispositional negativity, as indicated here by IU and TA, is associated with greater responding to threat cues during the beginning of delayed extinction, and, thus, demonstrates altered learning patterns under changing environments.

Learning Multiple Movements in Parallel-Accurately and in Random Order, or Each with Added Noise?

Traditionally, studies on learning have mainly focused on the acquisition and stabilization of only single movement tasks. In everyday life and in sports, however, several new skills often must be learned in parallel. The extent to which the similarity of the movements or the order in which they are learned influences success has only recently begun to attract increased interest. This study aimed to compare the effects of CI in random practice order (high CI) with differential learning (DL) in learning three volleyball skills in parallel. Thirty-two advanced beginners in volleyball (mean age = 24, SD = 2.7) voluntarily participated in the study. Within a pre-, post-, retention test design, an intervention of six weeks and one week retention phase, the effects of three practice protocols of a CI, DL, and control (CO) group were compared. Three different volleyball skills (underhand pass, overhand pass, and overhand serve) were trained with emphasis on accuracy. Results showed statistically significant higher rates of improvement in the acquisition and learning phases for the DL group compared to the CI and CO groups. The differences were associated with moderate to high effect sizes in all individual skills and in the combined skills. The findings show more agreement with DL than with CI theory.

Personality development in adolescence: Examining big five trait trajectories in differential learning environments

This study addresses two questions: Do Big Five traits change from early to middle adolescence? How do developmental trajectories differ between educational environments (i.e., secondary school tracks)? We used a representative random sample from Germany, following 6th graders (T1, N = 1662; age: M = 11.68; SD = 0.63; female = 47.4%; from N = 87 primary schools) to the end of compulsory secondary education over three measurement points (i.e., assessing students in 6th, 7th, and 9th grade). Results of latent change modeling indicated overall increases in mean levels for all personality traits from early to middle adolescence. Regarding selection effects, the results indicated that more conscientious, agreeable, extraverted, open, and emotionally stable sixth graders were more likely to transit into an academic rather than a non-academic secondary school track. Moreover, the results showed that these initial differences in personality traits seemed to diminish between 6th and 9th grade for all personality traits. Additionally, controlling for selection effects, a contextual effect was discernible for conscientiousness. This study highlighted the potential role of differential learning environments for modifying changes in personality traits during adolescence.