Career guidance as an institutionalized pedagogical practice carries a range of aspirations and goals aimed at supporting individuals through various transitions in education and the labor market. In this article, we examine one of the most recent policy goals and reforms in Finland: the extension of compulsory education until the age of 18 years, which made it statutory for lower secondary schools to provide a new form of guidance — ‘intensified personal guidance counseling’ (TEHO)—for pupils uncertain about where to apply for upper secondary education. We explore guidance counselors’ perspectives on TEHO and how they have implemented it. We ask: What do counselors perceive as TEHO’s objective, how does this relate to the tools and arrangements chosen for cooperation — who is involved in TEHO guidance, and how? We approach guidance with activity theory as our theoretical lens. This approach provides conceptual tools not only for object-oriented analysis of guidance but also for developing guidance in schools, considering the wider context that frames it. Drawing on interviews with guidance counselors (N = 35), our study demonstrates that despite the aim to support human growth and pupil’s agency holistically, TEHO can be narrowed down to an instrument that supports educational transitions. Nonetheless, our research highlights promising guidance practices that grasp the pupil’s life situation and provide guidance from that starting point, thus responding to needs highlighted by critical guidance research. Abstrakti Ohjaukselle asetetaan tänä päivänä paljon yhteiskuntapoliittisia tavoitteita yksilöiden tukemisesta erilaisissa koulutus- ja työmarkkinasiirtymissä. Yksi tuore esimerkki tästä Suomessa on oppivelvollisuuden laajentaminen 18 ikävuoteen saakka. Sen yhteydessä peruskouluille asetettiin velvollisuus tarjota tehostettua henkilökohtaista oppilaanohjausta nuorille, jotka ovat epävarmoja tulevaisuudensuunnastaan. Artikkelissa kysymme, miten oppilaanohjaajat ovat lähteneet viemään tätä uutta tehostettua ohjausta käytäntöön ja keille sitä kohdennetaan. Tutkimuksessa lähestytään ohjausta kohteellisena toimintana. Lähestymistapa antaa käsitteellisiä välineitä myös siihen, miten ohjaustoimintaa voi kehittää koulussa huomioiden sitä kehystävän laajemman toimintaympäristön. Aineistona on 35 oppilaanohjaajan haastattelut. Analyysimme osoittaa, että oppilaan kasvua tukevista holistisista tavoitteenasetteluista huolimatta tehostettu ohjaus kapenee koulutussiirtymän varmistajaksi. Toisaalta tutkimuksemme nostaa esiin ohjauksen käytänteitä, jotka onnistuvat tavoittamaan nuoren sosiaaliskulttuurista elämäntilannetta ja jotka samalla vastaavat myös kriittisen ohjaustutkimuksen esiin nostamiin kipupisteisiin ohjauksen kehittämishaasteista. Asiasanat: opinto-ohjaus; uraohjaus; oppilaat; peruskoulu; toiminnan teoria; tehostettu henkilökohtainen oppilaanohjaus
Accelerating and universalizing senior-high-school education is critical for advancing educational equity, particularly in Asia where senior-high-school entrance examination policies (e.g., China's zhongkao) shape adolescent trajectories. Grounded in Human Capital Theory, this study employs unordered multinomial logistic regression to examine how adolescents' perceived parental expectations across five dimensions—academic performance, physical/mental health, future achievement, behavioral conduct, and interpersonal relationships—influence their post-junior-highschool pathway intentions in Western China (N = 1077). Key findings reveal: (1) Adolescents identified parental expectations as the primary determinant (78.3 % of respondents) of their tracking decisions; (2) each 1-point increase in perceived parental academic expectations significantly reduced dropout intention odds by 75.1 % (OR = 0.249, p* < .01), while lower parental expectations predicted stronger inclinations toward vocational pathways or workforce entry; (3) systemic gaps persist: 88% of adolescents lacked professional career guidance, with 92 % relying solely on teachers for zhongkao policy information. This research yields three primary contributions: An empirical framework for facilitating parent-adolescent expectation alignment, potentially mitigating transitional conflicts; robust empirical evidence supporting the enhancement of professional career guidance services; a cross-culturally adaptable methodology for adolescent development studies. Implications for educational equity initiatives and counseling practices in Asian contexts are discussed.
Punn Lertjaturaphat, Jungwoo Rhee, Jaewon You
et al.
The increasing popularity of microcontroller platforms like Arduino enables diverse end-user developers to participate in circuit prototyping. Traditionally, follow-along tutorials serve as an essential learning method for makers, and in fact, several prior toolkits leveraged this format as a way to engage new makers. However, literature and our formative study (N=12) show that makers have unique preferences regarding the construction of their circuits and idiosyncratic ways to assess and debug problems, which contrasts with the step-by-step instructional nature of tutorials and those systems leveraging this method. To address this mismatch, we present a prototyping platform that supports personalized circuit construction and debugging. Our system utilizes an augmented breadboard, which is circuit-aware and supports on-the-fly hardware reconfiguration via contextualized guidance and in-situ circuit validation through interactive tests. Through a usability study (N=12), we demonstrate how makers leverage circuit-aware guidance and debugging to support individual building patterns.
Christopher J. Beukes, Rènette du Toit, Gino Garach
et al.
The European Union (EU)-supported Education for Employability (E4E) Technical Assistance project in South Africa aimed to enhance youth employability by aligning education, training and employment systems. In partnership with the Department of Basic Education (DBE), Department of Higher Education and Training (DHET) and Department of Employment and Labour (DEL), it focused on cross-sector collaboration to strengthen pathways from learning to work, looking at the Three Streams Model in basic education, career guidance and development frameworks and systems, as well as bilateral labour agreements and a quota system.
Contribution: The project promoted both strategic and operational alignment, with an emphasis on coordinated implementation to improve outcomes for young people transitioning through education and training into the labour market. Career development provided a unifying thread across the three sectors’ efforts.
Vocational guidance. Career development, Social Sciences
Alexander Lobashev, Maria Larchenko, Dmitry Guskov
We propose SW-Guidance, a training-free approach for image generation conditioned on the color distribution of a reference image. While it is possible to generate an image with fixed colors by first creating an image from a text prompt and then applying a color style transfer method, this approach often results in semantically meaningless colors in the generated image. Our method solves this problem by modifying the sampling process of a diffusion model to incorporate the differentiable Sliced 1-Wasserstein distance between the color distribution of the generated image and the reference palette. Our method outperforms state-of-the-art techniques for color-conditional generation in terms of color similarity to the reference, producing images that not only match the reference colors but also maintain semantic coherence with the original text prompt. Our source code is available at https://github.com/alobashev/sw-guidance/.
This paper addresses a critical aerial defense challenge in contested airspace, involving three autonomous aerial vehicles -- a hostile drone (the pursuer), a high-value drone (the evader), and a protective drone (the defender). We present a cooperative guidance framework for the evader-defender team that guarantees interception of the pursuer before it can capture the evader, even under highly dynamic and uncertain engagement conditions. Unlike traditional heuristic, optimal control, or differential game-based methods, we approach the problem within a time-constrained guidance framework, leveraging true proportional navigation based approach that ensures robust and guaranteed solutions to the aerial defense problem. The proposed strategy is computationally lightweight, scalable to a large number of agent configurations, and does not require knowledge of the pursuer's strategy or control laws. From arbitrary initial geometries, our method guarantees that key engagement errors are driven to zero within a fixed time, leading to a successful mission. Extensive simulations across diverse and adversarial scenarios confirm the effectiveness of the proposed strategy and its relevance for real-time autonomous defense in contested airspace environments.
Understanding the dynamics of human-AI interaction in question answering is crucial for enhancing collaborative efficiency. Extending from our initial formative study, which revealed challenges in human utilization of conversational AI support, we designed two configurations for prompt guidance: a Nudging approach, where the AI suggests potential responses for human agents, and a Highlight strategy, emphasizing crucial parts of reference documents to aid human responses. Through two controlled experiments, the first involving 31 participants and the second involving 106 participants, we compared these configurations against traditional human-only approaches, both with and without AI assistance. Our findings suggest that effective human-AI collaboration can enhance response quality, though merely combining human and AI efforts does not ensure improved outcomes. In particular, the Nudging configuration was shown to help improve the quality of the output when compared to AI alone. This paper delves into the development of these prompt guidance paradigms, offering insights for refining human-AI collaborations in conversational question-answering contexts and contributing to a broader understanding of human perceptions and expectations in AI partnerships.
This paper proposes a cooperative integrated estimation-guidance framework for simultaneous interception of a non-maneuvering target using a team of unmanned autonomous vehicles, assuming only a subset of vehicles are equipped with dedicated sensors to measure the target's states. Unlike earlier approaches that focus solely on either estimation or guidance design, the proposed framework unifies both within a cooperative architecture. To circumvent the limitation posed by heterogeneity in target observability, sensorless vehicles estimate the target's state by leveraging information exchanged with neighboring agents over a directed communication topology through a prescribed-time observer. The proposed approach employs true proportional navigation guidance (TPNG), which uses an exact time-to-go formulation and is applicable across a wide spectrum of target motions. Furthermore, prescribed-time observer and controller are employed to achieve convergence to true target's state and consensus in time-to-go within set predefined times, respectively. Simulations demonstrate the effectiveness of the proposed framework under various engagement scenarios.
This paper presents an optimal control solution for an aerocapture vehicle with two control inputs, bank angle and angle of attack, referred to as augmented bank angle modulation (ABAM). We derive the optimal control profiles using Pontryagin's Minimum Principle, validate the result numerically using the Gauss pseudospectral method (implemented in GPOPS), and introduce a novel guidance algorithm, ABAMGuid, for in-flight decision making. High-fidelity Monte Carlo simulations of a Uranus aerocapture mission demonstrate that ABAMGuid can greatly improve capture success rates and reduce the propellant needed for orbital correction following the atmospheric pass.
This paper introduces a novel watermarking method for diffusion models. It is based on guiding the diffusion process using the gradient computed from any off-the-shelf watermark decoder. The gradient computation encompasses different image augmentations, increasing robustness to attacks against which the decoder was not originally robust, without retraining or fine-tuning. Our method effectively convert any \textit{post-hoc} watermarking scheme into an in-generation embedding along the diffusion process. We show that this approach is complementary to watermarking techniques modifying the variational autoencoder at the end of the diffusion process. We validate the methods on different diffusion models and detectors. The watermarking guidance does not significantly alter the generated image for a given seed and prompt, preserving both the diversity and quality of generation.
Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer variability, the autoPET challenge series seeks to advance automated segmentation methods in complex multi-tracer and multi-center settings. Building on this foundation, autoPET IV introduces a human-in-the-loop scenario to efficiently utilize interactive human guidance in segmentation tasks. In this work, we incorporated tracer classification, organ supervision and simulated clicks guidance into the nnUNet Residual Encoder framework, forming an integrated pipeline that demonstrates robust performance in a fully automated (zero-guidance) context and efficiently leverages iterative interactions to progressively enhance segmentation accuracy.
Diffusion models have demonstrated strong generative performance when using guidance methods such as classifier-free guidance (CFG), which enhance output quality by modifying the sampling trajectory. These methods typically improve a target output by intentionally degrading another, often the unconditional output, using heuristic perturbation functions such as identity mixing or blurred conditions. However, these approaches lack a principled foundation and rely on manually designed distortions. In this work, we propose Adversarial Sinkhorn Attention Guidance (ASAG), a novel method that reinterprets attention scores in diffusion models through the lens of optimal transport and intentionally disrupt the transport cost via Sinkhorn algorithm. Instead of naively corrupting the attention mechanism, ASAG injects an adversarial cost within self-attention layers to reduce pixel-wise similarity between queries and keys. This deliberate degradation weakens misleading attention alignments and leads to improved conditional and unconditional sample quality. ASAG shows consistent improvements in text-to-image diffusion, and enhances controllability and fidelity in downstream applications such as IP-Adapter and ControlNet. The method is lightweight, plug-and-play, and improves reliability without requiring any model retraining.
Loïck Degorre, Emmanuel Delaleau, Cédric Join
et al.
This work presents a new approach to the guidance and control of marine craft via HEOL, i.e., a new way of combining flatness-based and model-free controllers. Its goal is to develop a general regulator for Unmanned Surface Vehicles (USV). To do so, the well-known USV maneuvering model is simplified into a nominal Hovercraft model which is flat. A flatness-based controller is derived for the simplified USV model and the loop is closed via an intelligent proportional-derivative (iPD) regulator. We thus associate the well-documented natural robustness of flatness-based control and adaptivity of iPDs. The controller is applied in simulation to two surface vessels, one meeting the simplifying hypotheses, the other one being a generic USV of the literature. It is shown to stabilize both systems even in the presence of unmodeled environmental disturbances.
Denoising-based generative models, particularly diffusion and flow matching algorithms, have achieved remarkable success. However, aligning their output distributions with complex downstream objectives, such as human preferences, compositional accuracy, or data compressibility, remains challenging. While reinforcement learning (RL) fine-tuning methods, inspired by advances in RL from human feedback (RLHF) for large language models, have been adapted to these generative frameworks, current RL approaches are suboptimal for diffusion models and offer limited flexibility in controlling alignment strength after fine-tuning. In this work, we reinterpret RL fine-tuning for diffusion models through the lens of stochastic differential equations and implicit reward conditioning. We introduce Reinforcement Learning Guidance (RLG), an inference-time method that adapts Classifier-Free Guidance (CFG) by combining the outputs of the base and RL fine-tuned models via a geometric average. Our theoretical analysis shows that RLG's guidance scale is mathematically equivalent to adjusting the KL-regularization coefficient in standard RL objectives, enabling dynamic control over the alignment-quality trade-off without further training. Extensive experiments demonstrate that RLG consistently improves the performance of RL fine-tuned models across various architectures, RL algorithms, and downstream tasks, including human preferences, compositional control, compressibility, and text rendering. Furthermore, RLG supports both interpolation and extrapolation, thereby offering unprecedented flexibility in controlling generative alignment. Our approach provides a practical and theoretically sound solution for enhancing and controlling diffusion model alignment at inference. The source code for RLG is publicly available at the Github: https://github.com/jinluo12345/Reinforcement-learning-guidance.
تحقیق حاضر با هدف بررسی تأثیر محیط یادگیری در دروس کارآفرینی بر قصد کارآفرینانه باتوجه به نقش میانجی نگرش دانشجویان به کسبوکار انجام پذیرفت. این پژوهش برمبنای هدف از نوع کاربردی و از نظر روش پیمایشی است. جامعه آماری را دانشجویان تحصیلات تکمیلی رشتههای مدیریت و کارآفرینی دانشگاه رازی به تعداد 127 نفر تشکیل داد. براساس روش نمونهگیری سرشماری کلیه دانشجویان بهعنوان نمونه انتخاب شدند. ابزار گردآوری دادهها پرسشنامههای استاندارد محیط یادگیری در دروس کارآفرینی، قصد کارآفرینانه و نگرش دانشجویان به کسبوکار بود. روایی پرسشنامه ها با استفاده از نظرات متخصصان رشته مدیریت و پایایی آنها از طریق آلفای کرونباخ تأیید شد. داده ها با استفاده از مدل معادلات ساختاری و نرم افزار Smart PLS4 تحلیل شدند. یافتههای تحقیق نشان داد که در سطح خطای 05/0 محیط یادگیری در دروس کارآفرینی تأثیر مثبت و معناداری بر نگرش دانشجویان به کسبوکار و قصد کارآفرینانه آنها دارد. نتایج نشان داد که نگرش دانشجویان به کسبوکار سبب ارتقاء قصد کارآفرینانه آنها شده است. یافتهها نشان داد که محیط یادگیری بهصورت غیرمستقیم و از طریق تقویت متغیر «نگرش دانشجویان نسبت به کسبوکار» تأثیر معناداری بر قصد کارآفرینانه دارد (03/0 = P-value، 230/0 = γ). باتوجه به یافتههای تحقیق، آموزش کارآفرینی و فراهم نمودن محیط یادگیری دروس کارآفرینی و کسب دانش راهاندازی کسبوکار میتواند زمینه را برای توسعه توانمندیهای کارآفرینانه فراهم نموده و با تأثیر مثبتی که بر نگرش کارآفرینانه دانشجویان میگذارد، قصد کارآفرینی را در آنها تقویت مینماید.
Vocational guidance. Career development, Agriculture (General)
هدف: هدف پژوهش حاضر، الگویابی رابطه سرریزشدگی منفی و مثبت کار-خانواده با اشتیاق کاری و شادکامی با نقش میانجی استرس ادراک شده در شاغلین شهر اصفهان بود.روش: این پژوهش از نوع مقطعی-توصیفی و الگویابی معادلات ساختاری بود. جامعه آماری آن شاغلین شهر اصفهان بودند (1589044 نفر) که با استفاده از فرمول بنتلر و چو (1987) برای تعیین حجم نمونه در پژوهشهای معادلات ساختاری، 279 نفر به روش نمونه گیری دردسترس انتخاب شدند. برای گردآوری داده ها از مقیاس سرریزشدگی مثبت و منفی کار-خانواده گرزیواچ ومارکز(2000)، مقیاس استرس ادراک شده فرم4 سوالی وارتیگ، فورشاو، ساث و وایت (2013)، فهرست شادکامی آکسفورد هیلزوآرگایل (2002) و مقیاس اشتیاق کاری اوترخت- فرم کوتاه شافلی، باکروسالانووا (2006) استفاده شد. داده ها با استفاده از نرم افزار SPSS و AMOS نسخه 24 تحلیل شدند.یافته ها: یافته های پژوهش نشان داد که استرس ادراک شده در رابطه بین سرریزشدگی منفی کار-خانواده با اشتیاق کاری نقش میانجی کامل و در رابطه با شادکامی نقش میانجی جزئی دارد. رابطه مستقیم سرریزشدگی منفی وشادکامی نیزمعناداربود (17/0-β= و 01/0p<). رابطه مستقیم سرریزشدگی مثبت کار-خانواده بااشتیاق کاری (31/0+β= و 01/0p<) وشادکامی (35/0+β= و 01/0p<) معناداربود واسترس ادراک شده نقش میانجی دراین رابطه ایفانکرد. الگوی نهایی پژوهش، برازش مطلوبی باداده ها داشت (075/0+RMSEA= و 01/0p<) و 28درصدواریانس اشتیاق کاری و20 درصدواریانس شادکامی راتبیین کرد.نتیجه گیری: با توجه به مسیرهای متفاوت رابطه سرریزشدگی مثبت ومنفی بااشتیاق کاری وشادکامی ونقش متفاوت استرس ادراک شده دراین روابط،سازمانها باید اقدامات خودرا برمبنای این الگوها برای تاثیرگذاری حداکثری روی اشتیاق کاری و شادکامی تنظیم کنند.
This study deals with competence-based and student-centered Finnish initial vocational education and training (IVET). The main research question is how holistic guidance supports the building of the student’s competence identity in Finnish IVET. The holistic guidance in competence- and work-based vocational education and training is formally implemented when a personal competence development plan (PCDP) is drawn up for each student and informally in various interaction situations in studies, work, and leisure time. The guidance actors supported student’s personal growth, guided learning and provided career guidance. For this study, interviews were carried out with IVET students (n=15) and IVET teachers (n=29). The analysis carried out combined data-based and abductive qualitative content analysis methods. In the analysis results, the different guidance actors were organised into the following five levels of guidance: 1) IVET teachers, tutors, and workplace instructors, 2) IVET study counsellors and special needs teachers, 3) student welfare personnel, 4) leisure time actors and hobby instructors, and 5) employment specialists. Based on the results, a framework was designed for holistic student-centred guidance in Finnish IVET. This framework describes how active student agency and multidisciplinary guidance work can support the building and updating of the student’s competence identity. This study revealed that guidance processes are complex and highlights that many actors are, not only guidance specialists, needed to fulfil the task.
Crowd counting and localization have become increasingly important in computer vision due to their wide-ranging applications. While point-based strategies have been widely used in crowd counting methods, they face a significant challenge, i.e., the lack of an effective learning strategy to guide the matching process. This deficiency leads to instability in matching point proposals to target points, adversely affecting overall performance. To address this issue, we introduce an effective approach to stabilize the proposal-target matching in point-based methods. We propose Auxiliary Point Guidance (APG) to provide clear and effective guidance for proposal selection and optimization, addressing the core issue of matching uncertainty. Additionally, we develop Implicit Feature Interpolation (IFI) to enable adaptive feature extraction in diverse crowd scenarios, further enhancing the model's robustness and accuracy. Extensive experiments demonstrate the effectiveness of our approach, showing significant improvements in crowd counting and localization performance, particularly under challenging conditions. The source codes and trained models will be made publicly available.
Chiara Pozzi, Mauro Pontani, Alessandro Beolchi
et al.
The Gateway will represent a primary space system useful for the Artemis program, Earth-Moon transportation, and deep space exploration. It is expected to serve as a staging location on the way to the lunar surface. This study focuses on low-thrust transfer dynamics, from the Near-Rectilinear Halo Orbit traveled by Gateway to a specified Low-altitude Lunar Orbit (LLO). This research addresses: (i) determination of the minimum-time low-thrust trajectory and (ii) design, implementation, and testing of a guidance and control architecture, for a space vehicle that travels from Gateway to LLO. Orbit dynamics is described in terms of modified equinoctial elements, in the context of a high-fidelity ephemeris model. The minimum-time trajectory from Gateway to a specified lunar orbit is detected through an indirect heuristic approach, which uses the analytical conditions arising in optimal control theory in conjunction with a heuristic technique. However, future missions will pursue a growing level of autonomy, and this circumstance implies the mandatory design of an efficient feedback guidance scheme, capable of compensating for nonnominal flight conditions. This research proposes nonlinear orbit control as a viable option for autonomous explicit guidance of low-thrust transfers from Gateway to LLO. This approach allows defining a feedback law that enjoys quasi-global stability properties without requiring any offline reference trajectory. The overall spacecraft dynamics is modeled including attitude control and actuation. The latter is demanded to an array of reaction wheels, arranged in a pyramidal configuration. Guidance, attitude control, and actuation are implemented in an iterative scheme. Monte Carlo simulations demonstrate that the guidance and control architecture is effective with random starting points from Gateway and the temporary unavailability of the propulsion system.