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PERBANDINGAN MODEL PEMBELAJARAN MAKE A MATCH DAN CARD SORT TERHADAP HASIL BELAJAR IPS KELAS VIII Agustina Malan; Eka Adnan Agung; Musdalifah Musdalifah
EDUCATOR : Jurnal Inovasi Tenaga Pendidik dan Kependidikan Vol. 5 No. 3 (2025)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/educator.v5i3.6864

Abstract

This research was conducted to examine the differences in students’ learning achievements when applying two cooperative learning models, namely Make a Match and Card Sort. The study employed an experimental design with a quantitative approach and was carried out at SMP Negeri 1 Simbuang during the 2023/2024 academic year. The participants consisted of 23 students, randomly assigned into two groups using a simple lottery technique. The first group was taught through the Make a Match strategy, while the second group received instruction with the Card Sort strategy. Data were gathered using observation, documentation, and achievement tests, and further analyzed with the assistance of SPSS. The findings revealed that students taught through the Make a Match model obtained an average score of 71.27, whereas those taught using the Card Sort model achieved a higher mean score of 86.25. Results of the independent sample t-test indicated a significance value of 0.001, which is lower than 0.005, confirming a statistically significant difference between the two models. Consequently, it can be inferred that the Card Sort model demonstrates greater effectiveness in enhancing the learning performance of eighth-grade students at SMP Negeri 1 Simbuang compared to the Make a Match model. ABSTRAKPenelitian ini dilaksanakan untuk mengetahui perbedaan hasil belajar siswa dengan menggunakan dua model pembelajaran kooperatif, yaitu Make a Match dan Card Sort. Jenis penelitian yang dipakai adalah eksperimen dengan pendekatan kuantitatif, bertempat di SMP Negeri 1 Simbuang pada Tahun Ajaran 2023/2024. Subjek penelitian berjumlah 23 siswa yang ditentukan melalui teknik undian sederhana, sehingga terbentuk dua kelompok eksperimen. Kelompok pertama diberi perlakuan dengan model Make a Match, sedangkan kelompok kedua menggunakan model Card Sort. Proses pengumpulan data dilakukan melalui observasi, dokumentasi, serta tes hasil belajar, kemudian dianalisis menggunakan bantuan program SPSS. Hasil penelitian memperlihatkan bahwa nilai rata-rata hasil belajar siswa yang diajar dengan model Make a Match sebesar 71,27, sedangkan siswa yang belajar dengan model Card Sort memperoleh rata-rata 86,25. Uji beda menggunakan independent sample t-test menunjukkan nilai signifikansi 0,001 yang lebih kecil dari 0,005, menandakan adanya perbedaan yang signifikan antara kedua model. Dengan demikian, dapat disimpulkan bahwa penerapan model Card Sort lebih efektif dibandingkan Make a Match dalam meningkatkan capaian belajar siswa kelas VIII SMP Negeri 1 Simbuang.
LITERASI ARTIFICIAL INTELLIGENCE DAN KESIAPAN KERJA MAHASISWA TINGKAT AKHIR: STUDI PADA PROGRAM STUDI PENDIDIKAN EKONOMI UNIVERSITAS PATOMPO Sulfaidah Sulfaidah; Sitti Hajar Aswad; Musdalifah Musdalifah
Phinisi Integration Review Volume 9 Nomor 2 Tahun 2026
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/pir.v9i2.88464

Abstract

Perkembangan artificial intelligence (AI) mengubah karakteristik pekerjaan sekaligus memperluas kompetensi yang perlu dimiliki lulusan perguruan tinggi. Penelitian ini bertujuan menganalisis hubungan literasi AI dengan kesiapan kerja mahasiswa tingkat akhir Program Studi Pendidikan Ekonomi Universitas Patompo. Penelitian menggunakan pendekatan kuantitatif dengan desain korelasional cross-sectional. Sebanyak 114 mahasiswa tingkat akhir dilibatkan melalui total sampling. Literasi AI diukur berdasarkan dimensi use and apply AI, understand AI, detect AI, dan AI ethics, sedangkan kesiapan kerja mencakup personal characteristics, organizational acumen, work competence, dan social intelligence. Data dianalisis menggunakan statistik deskriptif dan regresi linear sederhana. Hasil menunjukkan rerata skor literasi AI sebesar 29,38 (SD = 10,21) dan kesiapan kerja sebesar 44,69 (SD = 17,04). Analisis regresi menunjukkan hubungan positif dan signifikan antara literasi AI dan kesiapan kerja (R = 0,577; F(1,112) = 55,758; p < 0,001). Literasi AI secara statistik menjelaskan 33,2% variasi kesiapan kerja (R² = 0,332), sedangkan koefisien regresi sebesar 0,963 menunjukkan bahwa setiap kenaikan satu poin literasi AI berasosiasi dengan peningkatan estimasi 0,963 poin kesiapan kerja. Temuan ini menegaskan bahwa literasi AI merupakan kompetensi relevan dalam persiapan transisi mahasiswa menuju dunia kerja, namun bukan satu-satunya determinan kesiapan kerja. Perguruan tinggi perlu mengintegrasikan penggunaan AI yang produktif, kritis, aman, dan etis dengan penguatan pengalaman kerja, soft skills, adaptabilitas karier, serta layanan pengembangan karier. Karena desain penelitian bersifat cross-sectional dan non-eksperimental, hasil tidak ditafsirkan sebagai hubungan sebab-akibat.Artificial intelligence (AI) is reshaping the nature of work and expanding the competencies expected from university graduates. This study examined the relationship between AI literacy and work readiness among final-year students in the Economics Education Study Program at Universitas Patompo. A quantitative cross-sectional correlational design was employed, involving 114 final-year students selected through total sampling. AI literacy was conceptualized through the dimensions of use and apply AI, understand AI, detect AI, and AI ethics, while work readiness comprised personal characteristics, organizational acumen, work competence, and social intelligence. Data were analyzed using descriptive statistics and simple linear regression. The mean AI literacy score was 29.38 (SD = 10.21), while the mean work readiness score was 44.69 (SD = 17.04). Regression analysis indicated a positive and statistically significant relationship between AI literacy and work readiness (R = .577; F(1,112) = 55.758; p < .001). AI literacy statistically accounted for 33.2% of the variance in work readiness (R² = .332). The regression coefficient (B = .963) indicated that a one-point increase in AI literacy was associated with an estimated .963-point increase in work readiness. The findings suggest that AI literacy is a relevant competence for supporting students’ transition into the labor market, although it is not the sole determinant of work readiness. Higher education institutions should integrate productive, critical, safe, and ethical AI use with work experience, soft skills, career adaptability, and career development services. Given the cross-sectional and non-experimental design, the findings should not be interpreted causally.