[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2957":3,"related-2957":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":39,"doi":40,"paper":41,"created_at":55},2957,"Multi-Criteria Evaluation Of Student Career Readiness Using An Intelligent Topsis-Based Decision Support System","https:\u002F\u002Fdoi.org\u002F10.59435\u002Fjocstec.v4i3.871","The assessment of student career readiness requires a systematic approach that considers multiple competencies and experiences relevant to workplace demands. This study develops an intelligent decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and map student career readiness. The study uses 20 student samples and five assessment criteria: field-specific competence (C1), internship experience (C2), communication skills (C3), digital literacy (C4), and English language proficiency (C5). Each criterion is assigned a weight based on its relative importance. The TOPSIS method involves decision matrix construction, normalization, weighted normalization, determination of positive and negative ideal solutions, distance calculation, and preference value calculation. The results show that Y11 achieved the highest preference value of 0.8296, followed by Y6 (0.7951), Y19 (0.7274), Y5 (0.7116), and Y2 (0.6999), while Y9 obtained the lowest preference value of 0.3112. Based on the classification results, 1 student (5%) was categorized as Highly Ready, 5 students (25%) as Ready, 12 students (60%) as Moderately Ready, and 2 students (10%) as Not Ready. These findings demonstrate that TOPSIS can effectively rank and classify student career readiness based on multiple assessment criteria. The proposed system can support higher education institutions in identifying students’ readiness levels and developing more objective, targeted, and data-driven career development strategies.","学生职业准备度的评估需要一种系统化方法，综合考虑与职场需求相关的多种能力和经历。本研究开发了一种基于逼近理想解排序法（TOPSIS）的智能决策支持系统，用于评估和映射学生的职业准备度。研究采用20个学生样本和五项评估标准：专业领域能力（C1）、实习经历（C2）、沟通能力（C3）、数字素养（C4）和英语语言能力（C5）。每项标准根据其相对重要性赋予权重。TOPSIS方法包括决策矩阵构建、归一化、加权归一化、正负理想解的确定、距离计算以及偏好值计算。结果表明，Y11获得了最高偏好值0.8296，其次是Y6（0.7951）、Y19（0.7274）、Y5（0.7116）和Y2（0.6999），而Y9获得了最低偏好值0.3112。根据分类结果，1名学生（5%）被归类为高度准备，5名学生（25%）为准备充分，12名学生（60%）为中等准备，2名学生（10%）为尚未准备。这些发现表明，TOPSIS能够基于多项评估标准有效对学生职业准备度进行排序和分类。所提出的系统可以支持高等教育机构识别学生的准备水平，并制定更加客观、有针对性和数据驱动的职业发展策略。",null,"Journal Of Computer Science And Technology (JOCSTEC)","2026-09-17T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究基于TOPSIS的学生职业准备度评估，属教育信息化领域，与三农、农业信息化、智慧农业、数字乡村等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":35},"I2",11,true,[33],"decision support",[33],[],"农业相关性需要复核",[],"Multi-CriteriaEvaluationOfStudentCareerR-2957",1,"10.59435\u002Fjocstec.v4i3.871",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":15,"oa_url":46,"card":47,"direction":53,"ingested_from":54},"W7213442254",[44],{"name":45,"orcid":9},"Revi Gusriva","https:\u002F\u002Fjurnal.padangtekno.com\u002Findex.php\u002Fjocstec\u002Farticle\u002Fdownload\u002F871\u002F550",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"用TOPSIS多准则决策系统评估20名学生的职业准备度并排序分类。","TOPSIS方法，5项准则，20个学生样本，加权决策矩阵。","Y11得分最高0.8296，60%学生为中等准备，仅5%高度准备。","农业人工智能与决策模型","可将TOPSIS多准则决策迁移至农业人才或新型职业农民能力评估与精准培训。","数字乡村与农业信息化","openalex","2026-09-19T23:30:41.966555Z",{"total":15,"page":39,"page_size":39,"items":57},[]]