{
      "divisions": [
        "pps_lit_evazdik"
      ],
      "keywords": "program evaluation, Fuzzy C-Means, TPACK, internship, I–U\r\ncollaboration",
      "rev_number": 8,
      "department": "Penelitian dan Evaluasi Pendidikan",
      "metadata_visibility": "show",
      "lastmod": "2026-06-25 02:16:38",
      "creators": [
        {
          "name": {
            "family": "Ndayizeye",
            "lineage": null,
            "honourific": null,
            "given": "Oscar"
          }
        },
        {
          "name": {
            "family": "Putro",
            "given": "Nur Hidayanto Pancoro Setyo",
            "lineage": null,
            "honourific": null
          }
        }
      ],
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-06-25 02:16:38",
      "uri": "http:\/\/eprints.uny.ac.id\/id\/eprint\/90799",
      "dir": "disk0\/00\/09\/07\/99",
      "documents": [
          {
            "placement": 1,
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                  "filename": "disertasi_oscar ndayizeye_22701261029.pdf",
                  "fileid": 1403715,
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                  "mtime": "2026-06-25 01:53:10"
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            "mime_type": "application\/pdf",
            "uri": "http:\/\/eprints.uny.ac.id\/id\/document\/677135",
            "eprintid": 90799,
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            "language": "en",
            "docid": 677135,
            "rev_number": 2
          }
      ],
      "title": "SMART KB-RLB Evaluation Model for Interns’ Soft Skills  Enhancement in The Industry–University Collaboration.",
      "userid": 1290,
      "type": "thesis",
      "institution": "Sekolah Pascasarjana",
      "ispublished": "pub",
      "thesis_type": "disertasi",
      "full_text_status": "restricted",
      "date": "2026-01-29",
      "datestamp": "2026-06-25 02:16:38",
      "eprintid": 90799,
      "abstract": "This research aimed to 1) design the SMART KB RLB internship evaluation\r\nmodel susceptible to evaluate internship program soft skills enhancement in the\r\nIndustry-University collaboration, 2) prove the model's feasibility and effectiveness,\r\n3) determine the model’s instruments' psychometric quality, 4) demonstrate interns’\r\nsoft skills enhancement in the industry–Universitas Negeri Yogyakarta collaboration,\r\nand 5) prove the level of TPACK integration in the internship program.\r\nThis is an R&D project that merges the evaluator’s brand KB with SMART data\r\nanalysis using the Fuzzy C-Means Machine Learning algorithm with R-Reaction, L-\r\nLearning, and B-Behaviour (henceforth, KB-RLB) to develop a higher education\r\ninternship program evaluation model. As the focus was on general soft skills\r\nenhancement, the research took place at Yogyakarta State University, specifically in\r\nthe Faculty of Languages, Arts, and Culture (FLAC), within the English Literature and\r\nIndonesian Literature Study Programs. The research subjects included interns,\r\nacademic supervisors, and internship coordinators (n = 120). The data collection\r\ntechniques included a questionnaire, a situational judgement test (SJT), and\r\ndocumentation. RStudio version 2025.09.2+418 was used to analyse quantitatively the\r\npsychometric validation (CTT, IRT, CFA) and fuzzy C‑means clustering to profile\r\ninterns’ mastery of soft skills, while data reduction, condensation, and verification\r\nwere used to analyse the qualitative data.\r\nThe R&D research results indicate that the SMART KB-RLB model design\r\n1) includes the following components: reaction, learning of seven soft skills,\r\nbehaviour, TPACK integration, instruments that satisfy psychometric properties\r\nstandards, Feedback, and SMART Data Analysis through algorithmic interns’ soft\r\nskills mastery clustering, all of which enable program recommendations. The model is\r\nfound to be 2) feasible as it can be logistically and contextually applied in a real-world\r\ninternship program soft skills enhancement evaluation without significant barriers; it\r\nis also effective as it helped to profile interns’ soft skills mastery, it identified aspects\r\nto optimise, is cost and time-effective, and environment-friendly as most data are\r\ncollected online and available in electronic form. In terms of the 3) model’s quality,\r\nthe psychometric property analysis indicates strong validity (Aiken’s V≈ 0.86),\r\nmoderate reliability (CR ≈ 0.70), and good CFA fit (CFI\/TLI > .93; RMSEA = .035;\r\n<0.14 <SRMR <0.08); IRT parameters indicate fair-to-good discrimination and\r\nmoderate item difficulty (b = –1.790 to 0.330). As evidence of soft skills enhancement,\r\n4) the Fuzzy C-Means clustering identified two intern profiles: “Excellent” (46.2%,\r\n≥85, that is, an A) and “Moderate” (53.8% of interns, have 79≤ score <83, a B score);\r\ncommunication skills emerged as the strongest predictor of supervisor and overall\r\ninterns’ scores, with time management soft skills correlating strongly with problem-\r\nsolving (r = 0.57). 5) TPACK integration is still basic as it is used for production and\r\ncommunication; it is not yet at the pedagogical transformative level.",
      "subjects": [
        "D4",
        "ep"
      ]
    }