Expert Judgment Research Articles

Evaluative practice and its impact on teaching performance of the faculty of engineering of a public university of Peru 2023

The general objective of the research was to determine to what extent evaluative practice affects teaching performance in the Faculty of Engineering at a public university in Peru 2023. The type of research was basic, quantitative approach, non-experimental causal correlational design of transectional cut; the sample consisted of 50 teachers of the Faculty of Engineering of a Public University; two questionnaires were used, valid and reliable to expert judgment, to process the results the statistical program SPSS V26 was used. Among the results it was found that for 58.0% (29 teachers) the evaluative practices were at a good level; and for 64.0% (32 teachers) the teaching performance reached a high level. It was concluded that evaluative practices significantly influenced 73.2% of teacher performance; due to the Nagelkerke value of 0.732 and a regression significance of less than 1% (p < 0.01). It is the teacher's job to develop good evaluative practices that allow him/her to have a high level of teaching performance, reflected in the skills, knowledge and behaviours that students acquire.

Impact of Root Cutting on Acer platanoides and Tilia cordata Tree Stability in Urban Parks: A Case Study in Quebec City, Canada

Trees growing in urban environments are often impacted by maintenance or construction work involving the cutting of roots. Tree protection zones have been proposed to avoid critical damage to the tree. However, despite incorporating quantitative information, they heavily rely on expert judgement that remains to be validated. In a study conducted across six parks in Quebec City, Canada, two commonly found tree species, Acer platanoides L. and Tilia cordata Mill., presumed to be different in terms of vulnerability to root damage, were subjected to a range of trenching treatments. The trees were between 23 and 40 cm diameter at breast height (DBH). A safety factor was calculated relating the turning moment the tree can withstand to the turning moment imposed by high winds likely to occur. The safety factor against uprooting was assessed for each tree before and after root trenching using a non-destructive pulling approach. The effects of tree species, distance to the trench, and their combined interaction were tested on tree stability. The relationship between tree stability and soil texture, tree characteristics, and the number of damaged roots were also tested. Safety factors were initially variable, ranging from 0.5 to 4.5. T. cordata safety factors were lower than those of A. platanoides and influenced by soil texture. Trenching treatments had no effect on the safety factor, even when two perpendicular trenches were dug at 1 m from the stem. No index of the amount of root damaged was significantly related to the safety factor. Root trenching treatments that encroached closer to the tree trunk than the recommended tree protection zones did not affect the stability of both species. Nevertheless, it is essential to recognize that other ecophysiological processes might still be influenced, and long-term monitoring is crucial. Both should be taken into account when determining these zones.

Reconciling the Mismatch: Creating a Regenerative Framework for Regional Planning

The world is urbanizing rapidly, and many urbanized regions deplete and degrade their environment. The additional polycrisis of climate change, biodiversity loss, epidemics, food insecurity, and reduced water, air, and soil quality asks for a transformational vision for the design and planning of these urban regions. Current planning practices are not able to respond to the complexity of the problems associated with the polycrisis. At the same time, regenerative thinking has not yet been practical enough to be accepted into spatial planning practices and create regenerative regions that can respond to the global polycrisis. This mismatch reinforces the status quo of well-thought-through regenerative frameworks on the one hand and ongoing spatial planning in urban regions on the other. The aim of this study is to create a regenerative framework for regional planning. A range of regenerative frameworks have been analyzed and integrated into one ’framework of frameworks’, highlighting clusters of attributes describing the ‘what’, ‘when’, ‘how’, and ‘who’ of what needs to happen to create a regenerative world. On the other hand, expert judgement of planning practices in nine urban regions around the world provided insight about the priorities for regional regenerative development. This clarified the ‘what’ and ‘where’ of different aspects of urban planning in the region. Bringing the theoretical frameworks and practical understanding of urban planning together, the regenerative framework for regional planning provides a practical approach for navigating the complexities of creating a regenerative region. It starts by backtracking to the 1st generation city (of first settlements and indigenous understanding of the land). When this equilibrium is found, it is used to create a vision for the 3rd generation city (in which regenerative potentials are fully used). The comparison with the 2nd generation city (a current anthropogenic industrial city) clarifies what must change, how this change can be achieved, and who the agents of change are to make it happen.

A novel framework for quantitative resilience assessment in complex engineering systems during early and late design stages

Recently, resilience assessment has evolved and grown in prominence, yet most studies are carried out on operational stage when sufficient knowledge on the processes is available, overlooking the design stage, a time frame that is more suitable for making a resilient system. To this end, this work aims at developing a novel quantitative resilience assessment framework for engineering systems with two different approaches that practically analyse resilience at the early and late design stages when detailed information on the system’s safety and resilience capabilities may be deficient. In the early design stage, system resilience attributes are identified, and expert judgment is used to assess their quality. In the late design stage, attributes are derived from revealed information such as detailed emergency response and safety barrier data. In both stages, dynamic Bayesian Network (DBN) is used to quantify resilience based on the acquired information. Since the green hydrogen technology is relatively novice, the application of the proposed framework is demonstrated in a resilience assessment of a green hydrogen plant undergoing hydrogen release scenarios. The proposed framework can be used as an effective tool for early design improvements as well as enhancing process safety in the late design stage of hydrogen plants or any other complex engineering system.

Sextortion, Gender, and Digital Crime: A Socio-Legal Comparison between Positive and Islamic Law

Digital sexual violence with the motive of extortion, known as sextortion, is one of the gender-based violence online types that is rampant in nowadays era of technology. The purpose of this study is to analyze (theoretically and empirically) the sextortion while offering progressive socio-legal interpretation solutions. This study is a normative and empirical legal research, employing statutory, case study, interviews, Focus Group Discussion (FGD), and fiqh approach. The data sources both primary and secondary, consist of legal materials, cases, real experiences, and sociological overviews on some relevant points. The data analysis technique used is content analysis, socio-legal explorations, and surveys, namely by examining documents in the form of legislation and related court decisions, expert judgments and empirical views of the purposive expert community. The results of the study show that positive law still has a dual attitude in which digital sexual violence with extortion motives is considered a crime on one hand but resulting in weak sanctions on another. Women are potentially oppressed and extorted, while their cultural defense mechanism is structurally weak. Meanwhile, in Islamic law, sextortion can be categorized as a part of jarīmah (criminal act) with a punishment named ta’zīr, in which the government becomes the one who determines the type of punishment. A progressive socio-legal interpretation is therefore necessary for that specific type of crime.

Risk assessment of emergency operations of floating storage and regasification unit

As the receiving terminal of liquefied natural gas (LNG), the efficient emergency response of the floating storage and regasification unit (FSRU) is crucial to ensure the safety of LNG transportation at sea. However, few existing literature study the risk issues of FSRUs during emergency operations. In order to improve the emergency response capability of FSRU, this study proposes an innovative assessment method to identify hazards, quantify and rank the risks associated with emergency response and disposal operations of FSRU accidents. Firstly, a comprehensive index hierarchy system applicable to human, equipment, environment, and management aspects of emergency response and disposal operations of FSRU accident is established through an extensive literature review, analysis of accident reports, and expert judgments. Secondly, based on the concept of Intuitionistic Fuzzy Numbers, the Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) operator is used to enhance the conventional FMEA approach. This method considers the varying levels of expert confidence and integrates subjective and objective weights of risk influential factors (RIFs), and the efficacy is validated through sensitivity analysis. Finally, a comprehensive evaluation model employing the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation algorithms is used to aggregate the risk values of RIFs. The findings of this study offer decision-makers insights into risks during emergency operation, provide valuable guiding strategies for FSRU accident management, and improve the capability for emergencies at sea.

Detection and Exclusion of False-Positive Molecular Formula Assignments via Mass Error Distributions in UHR Mass Spectra of Natural Organic Matter.

Ultrahigh resolution mass spectrometry (UHRMS) routinely detects and identifies thousands of mass peaks in complex mixtures, such as natural organic matter (NOM) and petroleum. The assignment of several chemically plausible molecular formulas (MFs) for a single accurate mass still poses a major problem for the reliable interpretation of NOM composition in a biogeochemical context. Applying sensible chemical rules for MF validation is often insufficient to eliminate multiple assignments (MultiAs)─especially for mass peaks with low abundance or if ample heteroatoms or isotopes are included - and requires manual inspection or expert judgment. Here, we present a new approach based on mass error distributions for the identification of true and false assignments among MultiAs. To this end, we used the mass error in millidalton (mDa), which was superior to the commonly used relative mass error in ppm. We developed an automatic workflow to group MultiAs based on their shared formula units and Kendrick mass defect values and to evaluate the mass error distribution. In this way, the number of valid assignments of chlorinated disinfection byproducts was increased by 8-fold as compared to only applying 37Cl/35Cl isotope ratio filters. Likewise, phosphorus-containing MFs can be differentiated against chlorine-containing MFs with high confidence. Further, false assignments of highly aromatic sulfur-containing MFs ("black sulfur") to sodium adducts in negative ionization mode can be excluded by applying our approach. Overall, MFs for mass peaks that are close to the detection limit or where naturally occurring isotopes are rare (e.g., 15N) or absent (e.g., P and F) can now be validated, substantially increasing the reliability of MF assignments and broadening the applicability of UHRMS analysis to even more complex samples and processes.

ENTREPREUNERSHIP TRAINING TO ENHANCE THE ENTERPRENEURIAL KNOWLEDGE AN SKILL OF THE DEAF COMMUNITY

Disabilities have equal rights as Indonesian citizens, including employment and entrepreneurship rights. The implementation of policies regarding the obligation of institutions to employ people with disabilities is still far from expected. Limited job information and access to education and job training programs are challenges in realizing economic independence for disabled persons. Therefore, it is very important to conduct community service activities aimed at increasing the entrepreneurial knowledge and skills of the Deaf Community through entrepreneurship training in order to reach economic independence. Nine Deaf Friends and four Sign Language Interpreters were involved in this study. The training was carried out by online method for three sessions, once a week. The questionnaires were distributed to assess the participants' level of knowledge before and after training. Each participant was required to develop and present a business plan in order to improve their entrepreneurial abilities. Two expert judges evaluated and provided constructive feedback to all participants. The knowledge of participants before the training was 65.9 + 6.2, and increased by 12.4% to 74.1 + 10.8 after training. Participants were able to develop business proposals by the end of the program, with an average score of 7.6 + 0.7. Entrepreneurship training is able to enhance the entrepreneurial knowledge and skills of the Deaf Friends. This entrepreneurship training is a provision to assist the deaf community in creating and developing businesses that will lead to economic independence in the future.

A novel multi-attribute three-way decision model with three-parameter interval grey number decision-theoretic rough sets

PurposeTo overcome the limitations of traditional multi-attribute decision making (MADM) methods, which only provide deterministic rankings of decision objects, this paper proposes a novel multi-attribute 3WD model. This model presents three-parameter interval grey number decision-theoretic rough sets (TPIGNDTRSs), aiming to offer a reasoned interpretation of loss functions in grey environments and ensure objective assessment of conditional probabilities.Design/methodology/approachFirstly, the traditional equivalence relation is replaced with the probabilistic dominance relation (PDR), categorizing decision objects into two state sets in DTRS for more objective conditional probabilities. Secondly, as the three-parameter interval grey number (TPIGN) introduces the most probable value on the basis of the traditional two-parameter interval grey number, it provides a more comprehensive method for describing grey information. Consequently, integrating TPIGN into DTRS refines the interpretations of loss functions in grey environments. Finally, by utilizing two main sorting techniques, relative kernel and degree of accuracy ranking and possibility ranking, two types of 3WD rules with TPIGNDTRSs, are constructed.FindingsThis study has successfully developed and validated a new multi-attribute 3WD model. The model was tested in two distinct domains: evaluating innovation efficiency in high-tech enterprises and recommending movies in a practical case. The findings reveal that the model can effectively integrate relevant information of high-tech enterprises, provide the government with enterprise-level assessments, and gather consumer preferences to recommend the most suitable movies.Research limitations/implicationsThis study treats the loss function as grey information in the 3WD model but overlooks the grey nature of evaluation values, limiting its applicability. Additionally, the model’s reliance on subjective expert judgments and historical data to establish the loss function may affect its objectivity. The implications of this research are that the novel model overcomes traditional MADM limitations, enhancing decision-making quality and efficiency in complex and grey scenarios. The model’s successful application in evaluating high-tech enterprises and recommending movies illustrates its dual value in both theory and practice.Originality/valueInitially, the model proposed in this study is of significant importance for the development of the 3WD field, as it successfully addresses the challenges of uncertain loss functions and unknown conditional probabilities in grey information environments. Moreover, by integrating the 3WD model with MADM problems, it has broken through the bottlenecks of traditional MADM methods, offering new perspectives and strategies for solving MADM issues. Therefore, this research not only advances theoretical research but also provides powerful tools for practical applications.

Application of Statistical Learning Algorithms in Thermal Stress Assessment in Comparison with the Expert Judgment Inherent to the Universal Thermal Climate Index (UTCI)

Application of Statistical Learning Algorithms in Thermal Stress Assessment in Comparison with the Expert Judgment Inherent to the Universal Thermal Climate Index (UTCI)

Instrument Development for Study Programs Excellence Evaluation Based on Kirkpatrick Evaluation Model

Background: The study programs excellence stated in the institution's vision and mission acts as a guide for the institution in carrying out its activities. That excellence must be reflected in all aspects of academic activities. Evaluation of the implementation and achievement of study program’s excellence is important to fulfill the vision, and mission of the institution. This research aims to develop an evaluation instrument that is useful for monitoring the achievement of study program’s excellence. Methods: Two part of instrument development were carried out in this study, namely: instrument development, and instrument validation process. Instrument development part consist of two stage (item and scale development, and expert judgement). Meanwhile, instrument validation process part consist of 5 stages of comprehensive validation process by Sireci. This validation process used argument based validation concept. Results: This instrument uses the Kirkpatrick model which consists of 4 evaluation levels. Each level consists of several dimensions. A total of 55 instrument items were proven valid (r>0.217) and reliable (Cronbach’s Alpha>0.7). The instrument uses 2 types of data in the assessment process, namely primary data (measuring responses from the academic community using a questionnaire), and secondary data (assessment of document evidence). Conclusion: The instrument evaluation is prepared for the purpose of providing an overview of the implementation and achievement of institutional’s excellence. This is useful in providing valuable information to the institution to set strategies and policies to fulfill the institution's excellence.The instrument proved to be valid and reliable by an argument-based validation analysis.

Hybrid seismic vulnerability models for regional structures considering bivariate intensity measures

The multiple-probability decision-making method for earthquake hazards considers multiple performance indicators and can be used to effectively estimate the seismic risk of structures. The seismic vulnerability model directly affects the development of the earthquake risk distribution in regional structures. This paper innovatively developed a bivariate intensity measurement method based on macroscopic and instrumental seismic intensities. An optimized instrument intensity calculation model was established considering the actual monitoring data of historical earthquakes in China. A hybrid seismic vulnerability quantification method based on empirical, expert judgement and analysis has been proposed and applied to evaluate the vulnerability levels of three types of building clusters in two typical earthquakes in Qinghai Province, China (the 2008 Dachaidan (DCD) earthquake and the 2009 Haixi (HX) earthquake). A structural hybrid vulnerability matrix and surface for nine zones based on bivariate intensity measures were developed. A synthetic vulnerability membership index considering fuzzy failure criteria and decision theory was proposed, and vulnerability membership curves for different vulnerability levels were established. An improved earthquake economic loss prediction function is proposed, and synthetic economic vulnerability index prediction models for three typical buildings in different regions are established. The estimated and calculated results of the developed multiscale variable parameter synthetic vulnerability model are highly consistent with the actual earthquake loss observations.

Implementation Of M-Learning to Improve Learning Effectiveness In PAI Evaluation System Courses

The digital era greatly influences learning activities and facilitates access to learning materials anywhere and anytime. Flexibility of learning, high interactivity, personalization of learning are the basic considerations of IT utilization. The purpose of this research is to develop an M-learning-based lecture room application in PAI evaluation courses. This study is a research and development (R & D) study with the ADDIE model. The subjects of this study were 5th semester students of PAI Study Program as many as 132 people. Sampling using saturated sample technique. Data collection used observation, interview and questionnaire distribution. Product validity is assessed by experts and students as a feasibility test using independent sample t test analysis. Assessment by expert judgment shows that the value is very good, namely an average of 4.67 by media experts. The results of the study with independent sample t-test showed a significance value of 0.00 probability with a significance of 0.05, which means that there is a significant difference between classes that use discussions and classes that are accompanied by the lecture hall application. This product has a very practical assessment by students as a reference for independent learning with an average score of 3.78. This M-Learning contributes more effectively and efficiently if done outside the classroom as a companion to independent learning.

Ship collision risk assessment: A multi-criteria decision-making framework based on Dempster–Shafer evidence theory

Ship collision risk modelling is dominated by the utilization of the experts’ experiential insights and knowledge. To construct an effective assessment model for evaluating potential ship collision risks grounded in expert judgments, this paper introduces a novel multi-criteria decision-making framework based on Dempster–Shafer evidence theory (DSET). Firstly, a novel index-based assessment system is provided to generate the potential collision risk value of ships based on automatic identification system (AIS) data, which encompasses ship condition parameters, deviation coefficient, and frequency coefficient. Further, to generate the weights of these indicators, a framework to extract expert opinions is provided, which includes two parts: a) converting experts’ opinions into D-S framework through hesitant fuzzy linguistic terms and b) generating experts’ opinions using improved DSET. Regarding part a, a methodology is proposed to evaluate the trustworthiness of experts by considering both the level of uncertainty in their opinions and the degree of similarity between their viewpoints. Concerning part b, strategies are integrated to ascertain the most suitable weight for the basic probability assignment, thereby mitigating conflicts within the evidence. Finally, based on the collected AIS data, the proposed framework has been applied to six of the world's busiest waterways: (1) the Strait of Malacca, (2) the English Channel, (3) the Panama Canal, (4) the Suez Canal, (5) the Danish Straits, and (6) the Strait of Hormuz. Subsequently, comprehensive analytical results and recommendations are furnished, such as wide and smooth channels showing elevated risk.

Ground-motion models for earthquakes occurring in the United Kingdom

This article presents models to predict median horizontal elastic response spectral accelerations for 5% damping from earthquakes with moment magnitudes ranging from 3.5 to 7.25 occurring in the United Kingdom. This model was derived using the hybrid stochastic-empirical method based on an existing ground-motion model for California and a stochastic model for the UK that was developed specifically for this purpose. The model is presented in two consistent formats, both for two distance metrics, with different target end-users. Firstly, we provide a complete logic tree with 162 branches, and associated weights, capturing epistemic uncertainties in the depth to the top of rupture, geometric spreading, anelastic path attenuation, site attenuation and stress drop, which is more likely to be used for research. The weights for these branches were derived using Bayesian updating of a priori weights from expert judgment. Secondly, we provide a backbone model with three and five branches corresponding to different percentiles, with corresponding weights, capturing the overall epistemic uncertainty, which is tailored for engineering applications. The derived models are compared with ground-motion observations, both instrumental and macroseismic, from the UK and surrounding region (northern France, Belgium, the Netherlands, western Germany and western Scandinavia). These comparisons show that the model is well-centred (low overall bias and no obvious trends with magnitude or distance) and that the branches capture the body and range of the technically defensible interpretations. In addition, comparisons with ground-motion models that have been previously used within seismic hazard assessments for the UK show that ground-motion predictions from the proposed model match those from previous models quite closely for most magnitudes and distances. The models are available as computer subroutines for ease of use.

Weight of evidence evaluation of the metabolism disrupting effects of triphenyl phosphate using an expert knowledge elicitation approach

Identification of Endocrine-Disrupting Chemicals (EDCs) in a regulatory context requires a high level of evidence. However, lines of evidence (e.g. human, in vivo, in vitro or in silico) are heterogeneous and incomplete for quantifying evidence of the adverse effects and mechanisms involved. To date, for the regulatory appraisal of metabolism-disrupting chemicals (MDCs), no harmonised guidance to assess the weight of evidence has been developed at the EU or international level. To explore how to develop this, we applied a formal Expert Knowledge Elicitation (EKE) approach within the European GOLIATH project. EKE captures expert judgment in a quantitative manner and provides an estimate of uncertainty of the final opinion. As a proof of principle, we selected one suspected MDC -triphenyl phosphate (TPP) - based on its related adverse endpoints (obesity/adipogenicity) relevant to metabolic disruption and a putative Molecular Initiating Event (MIE): activation of peroxisome proliferator activated receptor gamma (PPARγ). We conducted a systematic literature review and assessed the quality of the lines of evidence with two independent groups of experts within GOLIATH, with the objective of categorising the metabolic disruption properties of TPP, by applying an EKE approach. Having followed the entire process separately, both groups arrived at the same conclusion, designating TPP as a “suspected MDC” with an overall quantitative agreement exceeding 85%, indicating robust reproducibility. The EKE method provides to be an important way to bring together scientists with diverse expertise and is recommended for future work in this area.

Methodology for Severe Convective Cloud Identification Using Lightweight Neural Network Model Ensembling

This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective cloud dataset by a combination of algorithms and expert judgment. Through the ablation study of a model ensembling combination of multiple specialized lightweight architectures—ENet, ESPNet, Fast-SCNN, ICNet, and MobileNetV2—the optimal EFNet (ENet- and Fast-SCNN-based network) not only achieves real-time processing capabilities but also ensures high accuracy in severe weather detection. EFNet consistently outperformed traditional, heavier models across several key performance indicators: achieving an accuracy of 0.9941, precision of 0.9391, recall of 0.9201, F1 score of 0.9295, and computing time of 18.65 s over the test dataset of 300 images (~0.06 s per 512 × 512 pic). ENet shows high precision but misses subtle clouds, while Fast-SCNN has high sensitivity but lower precision, leading to misclassifications. EFNet’s ensemble approach balances these traits, enhancing overall predictive accuracy. The ensemble method of lightweight models effectively aggregates the diverse strengths of the individual models, optimizing both speed and predictive performance.

STEM Students’ Motivation, Interest, and Career Direstion Amid New Normal Education: A Narrative Inquiry Research

The abrupt transition of learning modalities and the global health crisis brought by the COVID-19 pandemic have reshaped the learners’ experiences and insights, particularly in STEM (Science, Technology, Engineering, and Mathematics) education. Hence, this study was conducted to explore the stories of senior high school STEM learners on how the new normal education impacted their motivation, interest, and career direction using a narrative inquiry research design. Purposive sampling was used to select twelve participants from a public senior high school offering the STEM strand. A Semi-structured interview guide, which underwent expert judgment, was used to collect data. After securing consent forms, in-depth interviews were conducted via online platforms. Data collected were transcribed, coded, categorised, and thematised, applying Polkinghorne’s analysis of narratives. Results illustrated that students’ (a) motivation has declined, causing discouragement in STEM activities, however, their passion and goals kept them eager; (b) interest was elevated by their constant commitment to STEM endeavours, yet less enthusiasm has become evident; (c) career direction was still definite as fuelled by relevance, though some have restructured theirs due to the drastic change of circumstances. Furthermore, students devised adaptive strategies to further enrich themselves in STEM undertakings such as improving STEM identity, fostering STEM competence and habits, reorganisation of tasks, and peer collaboration. The findings of this study led to the creation of an action plan to further improve the STEM learning experiences of students amid the radical educational change. This research employed a holistic perspective as it considers the nature, aspect, and meaning of students’ narratives.

Universal Design for Learning in Second Language Teaching

This research was born in a foreign language teaching center in Barranquilla, Colombia, by taking the reality of this institution where teachers find themselves in front of diverse and neurodiversity students. They require orientation towards an inclusive education. The purpose of the study was to strengthen the teaching-learning process of a foreign language within the framework of inclusive education considering both neurodiverse and neurotypical students. The methodology is framed in mixed research, with interpretive paradigm and descriptive design in which a survey for teachers and a semi-structured interview for students were used. The results show that although teachers have many qualities, neurodidactic orientation is needed to attend and guarantee the accessibility, participation, permanence, communication and learning of the whole population regardless of their condition. From this need emerged the proposal "UDL in the teaching of foreign languages" which is materialized through the website "NeuroELE'', an online site that shares theoretical content to enrich the teaching knowledge with neuropedagogical bases, and neurodidactic strategies to strengthen the pedagogical work based on the Universal Design for Learning. The web page was evaluated by expert judges who highlighted its content and correspondence with the objective proposed with its design and it was subjected to changes generating a 2.0 version.

Development of The Automotive Technology Vocational Education Curriculum Based on Current Needs of The Automotive Industry

This research aims to: 1) Analyze the competencies needed by the current automotive industry in the Automotive Industry, 2) Analyze the implications of the automotive industry's needs for the automotive technology vocational education curriculum, and 3) Analyze the supporting and inhibiting factors of the partnership program for the automotive technology vocational education study program. The subjects of this research were 5 practitioners consisting of: Service Manager, Instructor, Service Advisor, and Mechanic from the Business and Industrial World (DUDI) in the automotive sector in the Special Region of Yogyakarta. This research is a qualitative descriptive study. Data collection techniques in this research used observation, documentation and interview methods. The research instrument was validated through expert judgment. The data analysis technique uses qualitative descriptive analysis with the triangulation method to obtain data validity. The research results show that: 1) the competencies required by the current automotive industry for undergraduate graduates include technical competency and managerial competency; 2) The need for technical and managerial competency demands that curriculum study materials be adapted to industry needs; 3) Supporting factors for the partnership program include: complete industrial facilities and infrastructure and adequate human resources (HR). The inhibiting factors for the partnership program are: the absence of an MoU between agencies and difficulties in preparing schedules.