4,402 research outputs found
Pengembangan Modul Statistika Deskriptif Berbasis Penalaran Statistik
Penelitian bertujuan mengembangkan modul statistika deskriptif berbasis penalaran statistik yang memenuhi kriteria valid, praktis, dan efektif. Penelitian pengembangan menggunakan tahapan ASSURE yang telah dimodifikasi sesuai dengan kebutuhan penelitian. Teknik pengumpulan data menggunakan angket validasi untuk mengukur kevalidan modul, angket respon mahasiswa calon guru untuk mengukur kepraktisan, dan tes untuk mengukur keefektifan. Uji kevalidan dan kepraktisan menggunakan rata-rata skor, dan uji keefektifan menggunakan uji binomial tes. Hasil pengembangan modul menunjukkan bahwa: modul yang dikembangkan telah memenuhi kriteria kevalidan dengan skor rata-rata 4,60, modul telah memenuhi kriteria kepraktisan dengan skor rata-rata angket respon sebesar 4,32, dan modul telah memenuhi kriteria keefektifan yang ditunjukan dengan 74% mahasiswa calon guru telah melampaui nilai 60. Dengan demikian ketiga kriteria (valid, praktis, dan efektif) dalam pengembangan modul telah tercapai. Namun demikian perlu optimalisasi penggunaan modul dalam pembelajaran dan pembiasaan pemberian permasalahan statistika penelitian maupun permasalahan kehidupan sehari-hari. 
Variability in University Studentsβ Use of Technology: An 'Approaches to Learning' Perspective
This study reports the results of a cross-case study analysis
of how studentsβ approaches to learning are demonstrated in
blended learning environments. It was initially propositioned
that approaches to learning as key determinants of the quality
of student learning outcomes are demonstrated specifically in
how students utilise technology in blended learning contexts.
Three case studies were conducted in a teaching-focused university
and the findings of each case were examined across
the case studies to determine their relatability. Prominent
themes from the cases showed that a deep approach can be
consistent with an intentionally selective use of facilities
within the online environment. Similarly, a strategic approach
can also be consistent with overall higher levels of online activity.
Conclusions highlight that approaches to learning within
a blended learning context are dependent on the level and
the quality of the face-to-face as well as online instruction
Lessons from University Instructors and Students Toward the Post-COVID-19 Laboratory Education
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μ¬λ²λν κ³Όνκ΅μ‘κ³Ό(ννμ 곡), 2023. 2. ννκΈ°.2020λ
μ λ°μν μ½λ‘λ-19 μ¬νμ μ΄λ‘ μΈν μ¬νμ 거리λκΈ° λ°©μ μ μ±
μ λν μ€ν μμ
λ€μ΄ κ΄μ΅μ μΈ λλ©΄ λ°©μμμ μ΅μνμ§ μμ λΉλλ©΄ λ°©μμΌλ‘ κ°μμ€λ½κ² μ νλλ μν©μ μΌκΈ°νμλ€. μ½λ‘λ-19λ‘ μΈν μΈκ³μ μΈ κ΅μ‘ κ²°μμ΄ μμλλ μν©μμ, κ³Όνκ΅μ‘νμλ€μ λΉλλ©΄ μ격 μ€ν μμ
μ΄ κ°μ Έμ¨ μ€ν κ΅μ‘μ λ³νμ μ£Όλͺ©νλ©° κ·Έ μ κ°μ κ²°κ³Όμ λν κ²½νμ μΈ μ°κ΅¬λ₯Ό μ΄κ΅¬νμλ€.
μ΄μ λ³Έ μ°κ΅¬μλ λ€μκ³Ό κ°μ λ κ°μ§ λͺ©νλ₯Ό μ§λκ³ μ°κ΅¬λ₯Ό μννμλ€. 첫째, μ격 μ€ν μμ
μ΄λΌλ μ΄μ μ μν©μ μ§λ©΄νμ¬ μ κΈ°λ μ€ν κ΅μ‘μ λ³Έμ§(essence)μ κ΄ν κ·Όλ³Έμ μΈ μ§λ¬Έλ€μ λ΅νκ³ μ νλ€. κ·Έλ¬ν μ§λ¬Έλ€μ λ€μκ³Ό κ°μ΄ μμ½λ μ μμ κ²μ΄λ€. (λ¬Έ 1) λνμ λ¬Όλ‘ K-12 κ³Όνκ΅μ‘μ μ΄λ₯΄κΈ°κΉμ§ μ€ν μμ
κ²½νμ λ³Έμ§μ 무μμΈκ°? λ§μ‘±μ€λ¬μ΄ νμ΅ κ²°κ³Όκ° μ΄λ μ λ 보μ₯λλ€λ©΄ μ격 λ§μΈμ¦μ¨ μμ
μ΄ νΈμ¦μ¨ κ²½νμ λ체ν μ μλκ°? (λ¬Έ 2) κ΅μμμ νμμ μ곡κ°μ 곡λ-μ‘΄μ¬(co-presence)λ νμμ μΈκ°? (λ¬Έ 3) μ°λ¦¬λ μ΄λ»κ² νμλ€μ μμ° νμμ λν νκ΅¬λ‘ μ΄λνκ³ , κ·Έκ²μ μ€ν λ³΄κ³ μμμ κ³Όνμ κΈμ°κΈ°λ‘μ νννλλ‘ ν μ μλκ°? (λ¬Έ 4) μμ λν λ΅μ μΈκ³μ μ¬λ¬ λ¬Έν λ° κ·Έμ λ°λ₯Έ κ΅μμμ νμ κ°μ μνΈμμ©μ νΉμ±μ λ°λΌ λ¬λΌμ§λκ°? (λ¬Έ 5) μ°λ¦¬λ μ΄λ»κ² μΌλ°μ μΈ μν©λΏ μλλΌ κΈ΄κΈν μν©μμλ μ€νν μ μλ ν¨κ³Όμ μ΄κ³ μ μμ μΈ μ€ν μμ
μ μ€κ³ν μ μλκ°? μ΄μ λν μ μ μ μΈ λ΅μ μ°κ΅¬μ μ΄λ‘ μ νκ³Ό ν¨κ» μ΄ν΄λ³΄κ³ , λ³΄λ€ μ§μ μ μΈ λ΅μ μ°κ΅¬μ κ²°κ³Όμ λΉμΆ λ
Όμμμ μ μνκ³ μ νμλ€.
λμ§Έ, λ³Έ λ
Όλ¬Έμ 2020λ
μ μ½λ‘λ-19λ‘ μΈνμ¬ μ΄λ°λ μ격 μ€ν μμ
μ κ΄νμ¬ λνμμμ μ΄κ³΅κ³μ΄ κ΅μ‘μ μ΄λ ν νμμ΄ λ°μνμλμ§λ₯Ό μ‘°μ¬νκ³ ν₯νμ λν μ격 μ€ν μμ
μ μν μ€μ μ μΈ ν¨μλ₯Ό μ 곡νλ μΌμ λͺ©νλ‘ νμλ€. λ³΄λ€ κ΅¬μ²΄μ μΌλ‘, λ³Έ λ
Όλ¬Έμ λν κ΅μμλ€μ΄ 2020λ
λ΄νκΈ°μ ν¬λ°λ―Ήμ μ§λ©΄νμ¬ μ΄λ»κ² μ격 μ€ν μμ
μ μ€ν(implement)νμλμ§λ₯Ό ν©λ¦¬μ μΌλ‘ μ€λͺ
νκ³ (μ°κ΅¬ 1), νμλ€μ λ°μμ ν΅ν΄ κ·Έ μ격 μ€ν μμ
μ κ²°κ³Όλ₯Ό μ‘°μ¬νλ©°(μ°κ΅¬ 3), λ―Έλμ λν μ격 μ€ν μμ
μ€κ³λ₯Ό μν μ€μ μ μΈ μ§μΉ¨(guideline)μ μ 곡νκ³ μ νμλ€. λ³Έ μ°κ΅¬μ νμ₯μΈ νκ΅λνκ΅(κ°λͺ
)μ μν©μ΄ μ΄λ¬ν μ λ°μ μΈ μ°κ΅¬μ μμκ³Ό μνμ κ°λ₯νκ² νμλ€.
μ΄λ‘ μ νλ‘μ, λν μ격 μ€ν μμ
μ μ€ν μμ
κ³Ό μ΄λ¬λ(e-learning)μ κ° μμκ° κ΅μ°¨νλ μ§μ μΌλ‘ μ΄ν΄νλ κ΄μ μ μ μνμλ€. μ°μ , μ€ν μμ
λλ μ΄λ¬λ μμ
μ μ€ννλ μ΄μ λ μ€ν μμ
μ λͺ©μ λλ μ΄λ¬λμ κ°λ₯μ± λ° μꡬμ λμ¬ μλ€. κ΅μ νλ‘κ·Έλ¨μ μΌμ’
μΌλ‘μ, μ€ν μμ
κ³Ό μ΄λ¬λμ μ΄λ»κ² λ΄μ©μ μ λ¬νκ³ , νμ΅μ κ° μνΈμμ©μ μ΄μ§νκ³ , νκ°μ νΌλλ°±μ μ 곡νλμ§λ₯Ό κ³ λ €ν΄μΌλ§ νλ€. κ·Έλ¦¬κ³ λ νλ‘κ·Έλ¨λ€μμ μ΄λ¬ν μΈ μμλ€μ μλ‘ μμ°μ€λ½κ² λμνλ€. 2020λ
μ λ€μν λν μ격 μ€ν μμ
λ€μ μ½λ‘λ-19 μν©μμ μ΄λ¬ν λ κ΅μ‘μ μ ν΅μ΄ λ§λμ, κ΅νΈνλ©°, νΌν©λ(blended) μ§μ μ΄μλ€. λν 2020λ
μ λ€μν λν μ격 μ€ν μμ
λ€μ νΉμ±μ μ¬νλ¬Ένμ μΈ μμλ₯Ό ν¬ν¨νλ κ°κ°μ κ΅μνμ΅ λ§₯λ½μμ νμ±λμλ€. 2020λ
μ λν μ격 μ€ν μμ
κ΅μμ λ° νμλ€λ‘λΆν° μ»μ κ΅νμ(μ°κ΅¬ 1 λ° 2) λ³Έ μ°κ΅¬μκ° μ€ν κ΅μ‘μ μνμ¬ νμ₯λ λΈλ λλ(blended) λ¬λ μ΄ν΄μ λλ¬νκ² νμμΌλ©°(2.3.4 μ°Έμ‘°) λν μ격 μ€ν μμ
μ μν κ΅μ μ€κ³(instructional design) λͺ¨νμ νμμ± μμ μ κΈ°νμλ€.
κ³Όνκ΅μ‘μμμ μ€ν μμ
μ κ΄νμ¬, μ€ν μμ
μ λͺ©μ κ³Ό, νΈμ¦μ¨(hands-on) λ° λ§μΈμ¦μ¨(minds-on) λ
Όμκ³Ό, μ€ν λ³΄κ³ μ μ°κΈ° λ° νΌλλ°± λ°©λ²μ κ³ μ°°νμλ€. μ΄λ¬λ λ° ν¨κ³Όμ μΈ κ΅μ μ λ΅μ κ΄νμ¬, μ΄λ¬λμ μ λ§ λ° μꡬμ, 맀체(media) μ μμ, μ¨λΌμΈ μνΈμμ©μ μμκ³Ό, μ΄λ¬λμμμ νκ° λ° νΌλλ°±μ μκ³ νμλ€. μ격 μ€ν μμ
μ (μ¬)μ°½λ°μ κ΄νμ¬λ μ½λ‘λ-19 μ΄μ κ³Ό μ΄νμ μ°κ΅¬λ€μ λμλ³΄κ³ , ν΄λΉ μ©μ΄μ μλ―Έλ₯Ό λμΆνμλ€. νΉλ³ν, μ격 μ€ν μμ
μ νμ₯λ λΈλ λλ λ¬λμΌλ‘ μ΄ν΄νλ κ΄μ μ μ μνμλλ°, μ΄λ μ²«μ§Έλ‘ νΈμ¦μ¨ λ° λ§μΈμ¦μ¨ μ€ν κ²½νμ νΌν©νκ³ λμ§Έλ‘ μ€ν κ²½νλ€κ³Ό νμ΅ κ³΅κ°λ€μ νΌν©νλ κ²μ΄μλ€.
λνμ¬, κ³Όνκ΅μ‘μμμ κ΅μμ νμ주체μ±(agency)μ νμ©νμ¬ λνμ μ΄κ³΅κ³μ΄ κ΅μμλ€μ΄ μ격 μ€ν μμ
μ μ€νν λμ μ μμ μΈ νλμ ν΄μνμλ€. μ°λ¦¬λλΌ κ³Όν κ΅μμλ€μ νμ주체μ±μ λν μ¬νλ¬Ένμ μκ°μ μ°κ΅¬μμ ν΄μμ μ§νμ κ±°μμ (macro-), μ€μμ (meso-), κ·Έλ¦¬κ³ λ―Έμμ (micro-) μμ€μ ꡬ쑰(structure)λ€λ‘ μ κ΅ννμλ€. λν, κ΅μ‘곡ν λΆμΌμμμ μ€κ³ λ° κ°λ° μ°κ΅¬ κ΄μ μ λ°λΌ μ μ°νκ³ (flexible) λ°λ³΅μ μΈ(iterative) κ΅μ μ€κ³ λͺ¨νμ μ μ©μ±μ μ μνμμΌλ©°, μ΄λ μΈμ νλΉνλ₯Ό μν μμ
λͺ¨λ λμΆ κ³Όμ μμμ λνΌλ νλ‘ν νμ΄ν(rapid prototyping)μ ν¬ν¨νλ κ²μ΄μλ€.
μ°κ΅¬ 1μμ, μ°κ΅¬μλ νκ΅λνκ΅μμ μ½λ‘λ-19 μ΄μ μ μλ‘ λΉμ·νμλ μΌλ° 물리ν, νν, μλ¬Όν, μ§κ΅¬κ³Όν μ€νλΏλ§ μλλΌ 2κ°μ μ 곡 κ΅κ³Ό μ€ν μμ
μ λΉκ΅νμλ€. μ°κ΅¬μλ λν μ격 μ€ν μμ
νμμ μ°½λ°μ μ¬νλ¬Ένμ κ΄μ μμ ν΄μνμλλ°, μ΄ λ μ½λ‘λ-19 ν¬λ°λ―Ήκ³Ό κ΅μ‘ λΉκ΅μ μνμ¬ λΆκ³Όλ ꡬ쑰 λ° λν κ΅μμλ€μ νμ주체μ±μ μ£Όλͺ©νμλ€. κ±°μμ μμ€μ νκ΅ λ§₯λ½, μ€μμ μμ€μ νκ΅λνκ΅ λ§₯λ½, κ·Έλ¦¬κ³ λ―Έμμ μμ€μ κ°λ³ λν μ격 μ€ν μμ
λ§₯λ½μ μλ‘ λΏλ§ μλλΌ λν κ΅μμμ νμ주체μ±κ³Όλ λ°μ νκ² μνΈμ°κ΄λμ΄ μμλ€. 2020λ
λ΄νκΈ°μ, κ΅μμμ νμ주체μ±μ μ΄λ¬ν λ€μΈ΅μ (multi-level) ꡬ쑰λ€μ μνμ¬ λͺ¨μμ§μ΄μ‘λ€(shaped). κ·Έλ¬λ, κ°λ³ κ΅κ³Ό(discipline)μ λ°λΌ μ€νλ λν μ격 μ€ν μμ
μ κ΅μμκ° ν¬μ
ν λ
Έλ ₯μ λ°λΌ μλΉν λ€μνκ² λμλ€. λν κ΅μμλ€μ κ³ λ €μ¬νμ λμμ μλ£, μ€ν λ°μ΄ν°μ νΉμ±, μμ λ€κ³Ό νμλ€ κ°μ μ νλ μνΈμμ©, νκ°μ μ΄λ €μ, κ·Έλ¦¬κ³ νμλ€μ΄ νΈμ¦μ¨ κ²½νμ΄ μμ΄ μ격 μ€ν μμ
μμ 무μμ μ»μ(gain) μ μλκ° νλ μ μ΄μλ€. 2020λ
κ°μνκΈ°λΆν° λν κ΅μμλ€μ μν©μ μ μνμ¬ μμ λ€μ μ격 μ€ν μμ
μ κ°μ νμμΌλ©°, λ λ§μ κ°μ μ λ€μ μ μνμλ€. μ°κ΅¬ 1μ κ²°κ³Όλ λν κ΅μμμ νμ주체μ±μ΄ μλ°ν κΈ΄κΈ μν©μμ λ€μν μ격 μ€ν μμ
μ€νμ΄ μ°½λ°νλ κ²°κ³Όλ₯Ό λ³μμμ 보μ¬μ€λ€.
μ°κ΅¬ 2λ μ°κ΅¬ 1κ³Ό λ°λ§μΆμ΄ νκ΅λνκ΅μμ μνλμλ€. μ°κ΅¬μλ λνμλ€μ΄ μλ‘ λ€λ₯Έ κ΅κ³Όμ λ€μν μ격 μ€ν μμ
κ²½νμ μ΄λ»κ² μΈμνμλμ§λ₯Ό μ‘°μ¬νμλ€. μ°κ΅¬ 2λ νΌν© μ°κ΅¬λ‘μ, 338λͺ
μ νμλ€λ‘λΆν° μ¨λΌμΈ μ€λ¬Έ μλ΅μ μ»μμΌλ©° 18λͺ
μ νμλ€κ³Ό μΈν°λ·°λ₯Ό μ€μνμλ€. λΆμ°λΆμ(ANOVA)κ³Ό Bonferroni μ¬ν κ²μ μ ν΅ν΄ μ격 μ€ν μμ
κ²½νμ λν νμλ€μ μΈμμ΄ κ΅κ³Ό(물리, νν, μλ¬Ό, μ§κ΅¬κ³Όν, λ€λ₯Έ μ 곡 κ³Όλͺ©)μ λ°λΌ ν΅κ³μ μΌλ‘ μ μλ―Ένκ² λ€λ₯΄λ€λ μ μ λ°κ²¬νμλ€(p < .05). λνμ¬, νμ μΈν°λ·°λ μ΄λ¬ν μ°¨μ΄λ€μ΄ κ°λ³ κ΅κ³Όλͺ©μμ μ°½λ°ν κ΅μ μ λ΅μ μνμ¬ λ°μνμμμ λλ¬λ΄μλ€. ν₯νμ ν¨κ³Όμ μΈ μ격 μ€ν μμ
μ μν μ λ΅μΌλ‘μ, μμ
μ λͺ©μ μ λͺ
νν μ€μ νκΈ°, μ€ν λμμμ μΈμ¬νκ² μ€κ³νκΈ°, λμμ (synchronous) μ¨λΌμΈ νλ ₯ μΈμ
μ 곡νκΈ°, μ€ν λ³΄κ³ μ μμ±μ λν νΌλλ°±μ μ 곡νκ³ λ³΄μΆ©μ νκ°λ₯Ό μ€μνκΈ° λ±μ μ μνμλ€.
μ°κ΅¬ 3μμ μ°κ΅¬μλ λν μ격 μ€ν μμ
μ μν λΈλ λλ μ€ν λ° μ΄λ¬λ κ΅μ μ€κ³(Blended Laboratory and E-learning iNstructional Design, BLEND) λͺ¨νμ κ°λ°νκ³ νλΉννμλ€. ν¬λ°λ―Ήμ μνμ¬ μλνλ κ΅μ νκ²½μ λμνκΈ° μν΄, μ°κ΅¬μλ κ΅μ μ€κ³ λͺ¨νμ μ μνκ² κ΅¬μΆνμ¬ μ€μ μ νμ΅ λ§₯λ½μ μ μ©νκ³ , μ°Έμ¬μμ νΌλλ°±μ ν΅ν λ°λ³΅μ (iterative) λͺ¨ν μμ μ μλνμλ€. μ°κ΅¬ λ§₯λ½μ μλΉ νν κ΅μ¬λ€μ μν λΆμννμ€ν κ°μ’μλ€. μ΄κΈ° BLEND λͺ¨νμ λ¬Έν 리뷰 λ° 2020λ
μ μ°κ΅¬ 1κ³Ό μ°κ΅¬ 2μ κ΅νμ κΈ°λ°νμ¬ λμΆλμλ€. λ΄μ (internal) νλΉνλ₯Ό μν΄ 6λͺ
μ μ΄ν΄λΉμ¬μ(stakeholder)κ° μ¬μ©μ± νκ°(usability test)μ μ°Έμ¬νμμΌλ©°, λ€μν κ³Όν κ΅κ³Ό λ°°κ²½μ 10λͺ
μ λ΄μ© μ λ¬Έκ°μ 3λͺ
μ κ΅μ‘곡ν μ λ¬Έκ°κ° μ λ¬Έκ° λ¦¬λ·°λ₯Ό μ 곡νμλ€. μΈμ (external) νλΉνλ₯Ό μν΄ ν΄λΉ μκΈ°μ κ΅μ μ€κ³ λͺ¨νμ κΈ°λ°μΌλ‘ λν μ격 μ€ν μμ
λͺ¨λμ΄ κ°λ° λ° μ€νλμκ³ , ν΄λΉ κ°μ’λ₯Ό μκ°νλ 7λͺ
μ λνμλ€μ΄ μ¨λΌμΈ μ€λ¬Έ λ° νμ μΈν°λ·°μ μ°Έμ¬νμλ€. 2νκΈ°μ νλΉν κ³Όμ μ κ±°μ³, BLEND λͺ¨νμ λ΄μ μΌλ‘ ν¨μ¨μ μ΄λ©°(efficient) μΈμ μΌλ‘ ν¨κ³Όμ (effective)μΈ κ²μΌλ‘ νλΉνλμλ€. μ΄ λ κ΅μμ λ° νμ κ°μ λμ μνΈμμ©μ΄ νΉλ³ν μ£Όλͺ©λμλ€. λν μ격 μ€ν μμ
μ μν μ΅μ’
BLEND λͺ¨νμ μ§μμ μΈ νμ± νκ°μ νΌλλ°±μ μ€μνλ©°, μ£Όλ³ κ·Έλ¦¬κ³ κ°μ’λ³ μμ€μμμ μ격 μ€ν μμ
κ΅μ 체μ λ₯Ό ꡬ쑰ννκ³ μκ°ννμλ€. μ°κ΅¬ 3μ κ³Όνκ΅μ‘μμ μ€κ³ λ° κ°λ° μ°κ΅¬ λ°©λ²μ μ μ©ν λλ¬Έ μ¬λ‘μ΄λ€.
λ³Έ μ°κ΅¬μμ λͺ¨λ ν΄κ²°λμ§ μκ³ μ¬μ ν νμ μ°κ΅¬λ₯Ό μꡬνλ μμ λ€μ λ€μκ³Ό κ°λ€: (1) μ격 μ€ν νμμ΄ μꡬνλ λ°μ κ°κ°μ κ³Όν κ³Όλͺ©(물리, νν, μλ¬Ό, μ§κ΅¬κ³Όν λ±)μ νΉμ± μ¬μ΄μ μνΈμμ©μ΄ λ μμΈν κ³ μ°°λμ΄μΌ νλ€. (2) μ€ν λμμμ μ΄λ»κ² μ€κ³νκ³ , 촬μνλ©°, νΈμ§ν΄μΌ νλμ§μ λ¬Έμ κ° μ¬μ ν μ€μνλ€. (3) κ°λ°©ν(open-ended) νꡬ μ€ν μμ
μ μν κ΅μ μ€κ³ λͺ¨νμ΄ ν₯νμ μ€μν μ°κ΅¬ μ£Όμ μ΄λ€. μ΄ κ²½μ°, κ°λ°©ν νꡬ μμ
νλ‘κ·Έλ¨μ μ΄λ»κ² νκ°ν κ²μΈμ§ μμ λ°λμ λ¨Όμ ν΄κ²°λμ΄μΌ ν μ°κ΅¬ μ£Όμ κ° λ κ²μ΄λ€.
λ³Έ μ°κ΅¬μ κ°μ μ 2020λ
λ° 2021λ
μ νκ΅λνκ΅λΌλ μ°κ΅¬ νμ₯μ λ
νΉμ±μ κΈ°μΈνλ€. λ³Έ μ°κ΅¬λ μ½λ‘λ-19 μ΄κΈ° μν©μμ μ°½λ°ν μ격 μ€ν μμ
μ κ΄νμ¬ μλΉν λ§μ λ°μ΄ν°λ₯Ό μμ§ν μ°κ΅¬ μ¬λ‘λ‘ λ³΄μΈλ€. κ·Έλ¬λ―λ‘, μ°κ΅¬ 1μμ μ°κ΅¬ 3μ μ΄λ₯΄λ μμ
μ μ½λ‘λ-19μ μ΄κΈ° λ¨κ³μμ λνλ μ격 μ€ν μμ
νμμ ν¬κ΄μ μΌλ‘ λ³΄κ³ νλ €λ μλλΌκ³ ν μ μλ€. νμ§λ§ μμ€μ μΌλ‘. λ³Έ μ°κ΅¬μ κ°μ μ λ§λ€μλ μ½λ‘λ-19 μν©μ μκ°μ΄ μ§λκ³ μν©μ΄ λ³νν¨μ λ°λΌ μλ μ κ²μΌλ‘ μμ©ν μ μλ€. κ²°κ³Όμ μΌλ‘, ν¬μ€νΈ-μ½λ‘λ-19 μλμ μ격 μμ
, νΉν μ격 μ€ν μμ
μ μ§μκ° μ΄λ ν μ§λ₯Ό μμνκΈ°λ μ½μ§ μλ€.
λ§μ½ μ°λ¦¬κ° λκ΄μ μΈ μμ μ μ·¨νλ€λ©΄, λν μ격 μ€ν μμ
μ λν μ°λ¦¬μ κ²½νμ μ€ν κ΅μ‘μ λν μ°λ¦¬μ μμμ νμ₯μμΌ, μκ°κ³Ό 곡κ°μ λλλ€λ©° λ€μν νμ΅ μμμ ν΅ν©νλ λΈλ λλνμμ ν₯ν΄ μ μ§νκ² ν κ²μ΄λ€. μ€μ λ‘, μ€ν κ΅μ‘μ μν΄ νμ₯λ λΈλ λλ λ¬λ μ΄ν΄λ νΈμ¦μ¨ λ λ§μΈμ¦μ¨, λμμ λ λΉλμμ , νμ₯ λ μ격 λ±μ μ€λ μ΄λΆλ²μ λμ΄ λ λμ μ€ν κ΅μ‘μΌλ‘ λμκ°λ κΈΈμ λΉμΆ λ©΄μ΄ μλ€.
μ΄μλ λ°λλ‘, λ§μ½ μ°λ¦¬κ° λΉκ΄μ μΈ μμ μ μ·¨νλ€λ©΄, μ격 μ€ν μμ
μ λν μ°λ¦¬μ μ¬κ°ν κ³ μ°° μμ μΈμ κ° μ¬λΌμ§ μ μμΌλ©°, μ΄λ κ΅μ‘μ¬μμ λ§μ κ΅μ λ°©λ²λ€μ΄ κ·Έλ¬νλ κ²κ³Ό λ§μ°¬κ°μ§μ΄λ€. κ·Έλ¬λ―λ‘, μκΈ°νμλ― μ½λ‘λ-19λ‘ μΈνμ¬ μ°λ¦¬κ° κ²½νν μ격 μ€ν μμ
μ ν΅ν΄ μ¬λ°κ²¬λ μ€ν μμ
μ λ³Έμ§μ κ΄ν κ·Όλ³Έμ μΈ μ§λ¬Έλ€(λ¬Έ 1-5)μ λ΅νλ μΌμ΄ μμ²λλ€. μ¬κΈ°μ μ΄λ¬ν μ§λ¬Έλ€μ λ΅νλ κ°μ₯ νΈλ¦¬ν λ°©λ²μ κ° μ€ν μμ
μμ μ νλ νμ΅ λͺ©νμ νΉμμ±μ μμ‘΄νλ κ²μ΄κ² μ§λ§, μ΄λ¬ν λ¨μν ν΄κ²°μ±
μ ν¬μ€νΈ-μ½λ‘λ-19 μ€ν κ΅μ‘μ μν λ μ¬νλ κ³ μ°°λ‘ λμκ°λ κΈΈμ μ΄μ΄μ€ μ μλ€.
κ·Έλ¬λ―λ‘, μμμ μ κΈ°λ 5κ°μ§μ μ§λ¬Έλ€μ λν΄ λ³Έ μ°κ΅¬μ μ°Έμ¬μλ€μ λͺ©μ리λ‘λΆν° λ³΄λ€ κ΅¬μ²΄μ μΈ λ΅μ ν΄λ³΄λ μΌμ΄ μλ―Έ μμ κ²μ΄λ€: (λ΅ 1) νμλ€μ΄ μ€ν κΈ°λ₯(skill)μ ν¨μν λΏλ§ μλλΌ μμνμ§ λͺ»νλ νμκ³Ό ν¨κ» μ묡μ μ§μ(tacit knowledge) λ° κ³Όνμ λ³Έμ±(nature of science)μ μ§λ©΄ν κΈ°νλ₯Ό μ 곡νκΈ° μνμ¬, νμλ€μκ² μ΅μλΆκ°κ²°μ νΈμ¦μ¨ κ²½νμ μ 곡ν΄μΌ νλ€. λΈλ λλ λ¬λ νμμ νΈμ¦μ¨ κ²½νκ³Ό λ§μΈμ¦μ¨ κ²½νμ λͺ¨λ κ°κ² νλ λμμ΄ λ μ μλ€. (λ΅ 2) κ΅μμμ νμλ€μ μκ°μ μΈ μΈ‘λ©΄μμλ λ°λμ λμμ μνΈμμ©μ ν΄μΌλ§ νλ€. λ€λ§, κ·Έ λ€μ΄ 곡κ°μ μΌλ‘ ν¨κ» μλ μΌμ΄ νμμ μΈμ§λ λͺ
ννμ§ μλ€. (λ΅ 3) λ§μ½ κ°λ₯νλ€λ©΄, νκΈ° λ¨μμ κ°λ°©ν μ€ν μμ
μ μ§ννλ κ²μ΄ νμλ€μ κΉμ΄ μλ νꡬμ μ¬κ³ λ‘ μ΄λνλ κ°μ₯ μ’μ κΈ°νκ° λ κ²μ΄λ€. νμ§λ§, νμ€μ μΌλ‘ μ리μ±
(cookbook) νμμ μ€ν μμ
λ€μμλ μ΄λ‘ μ μμΈ‘κ³Ό μ€μ μ€ν λ°μ΄ν° μ¬μ΄μ κ°κ·Ήλ§μ΄ νκ΅¬κ° μΌμ΄λκ² λλ μ μΌν μ§μ μΌ μ μλ€. κ·Έλ¬λ―λ‘, μλΉμ€ν(pre-lab) νλ, λ°μ΄ν° νΉμ±, λλ£ ν λ‘ (discussion)μ΄ μ£Όμ κΉκ² μ€κ³λμ΄μΌ νλ€. (λ΅ 4) λ§μ½ μ€ν μμ
νμ₯μ λλ¬μΌ λ¬Ένκ° μΈμ§μ κ²½λ‘λ‘μμ μ(hand) λλ λ§μ(mind)μ κ°μ‘°νκ±°λ, κ΅μμμ νμ κ°μ μνΈμμ©μ μμ§μ μΌλ‘ λλ μνμ μΌλ‘ λ§λ λ€λ©΄, κ·Έλ λ€κ³ ν μ μλ€. (λ΅ 5) κ΅μ 체μ μ λν νμ± νκ°λΌλ κ°λ
μ΄ μ€ν μμ
μ λ μ μμ μ΄κ³ (adaptive) μ μ°νκ² λ§λλ λ°©λ²μΌ μ μλλ°, μ΄κ²μ μ°κ΅¬ 3μμ κ°λ°λ BLEND λͺ¨νμμ μ λλ¬λλ€.
2020λ
νκ΅λνκ΅μ κ΅μμμ νμ΅μλ€μ λν μ격 μ€ν μμ
μ μ€ννκ³ μκ°νκΈ° μν΄ λ
Έλ ₯ν μ§μ ν νμμλ€(agents)μ΄μλ€. κ·Έλ¦¬κ³ κ·Έλ€μ΄ λ¨κΈ΄ κ΅νμ΄μΌλ§λ‘ ν¬μ€νΈ-μ½λ‘λ-19 μ€ν μμ
μ ν₯νλ BLEND λͺ¨νμ κ°λ° λ° μ€ν μμ
μ λ³Έμ§μ κ΄ν κ³ μ°°μ κ°λ₯νκ² νμλ€.The COVID-19 situation in 2020 and the so-called social distancing preventive policy necessitated the sudden shift of university laboratory courses from a conventional face-to-face format into an unfamiliar non-face-to-face one. Amidst the unexpected educational losses worldwide, science education scholars focused on the changes in laboratory education brought by remote laboratory course format and urged empirical studies on them.
The researcher had two research purposes throughout this study. First, it was to answer fundamental questions on the essence of laboratory education that were raised facing the unprecedented global implementation of remote laboratory courses. (Q1) What is the essence of the laboratory experience from the university to K-12 science education? If satisfactory learning outcomes are secured to some extent, can (remote) minds-on experience replace hands-on one? (Q2) Is spatio-temporal co-presence of instructors and students necessary? (Q3) How can we invite students to an inquiry about natural phenomena, which would be represented in their scientific writing in their lab report? (Q4) Do the answers differ according to the characteristics of interaction among instructors and students and in different cultures worldwide? (Q5) How can we design a laboratory course that is both effective and adaptive that can be implemented in both normal and emergency situations? The tentative answers were explored while reviewing theoretical backgrounds and more direct answers were given while discussing the specific results of this study.
Second, it was to investigate what happened in the university STEM education sites concerning remote labs necessitated by the COVID-19 in 2020 and provide implications for future University Remote Laboratories (URLs). More specifically, it was to rationalize how university instructors implemented their remote labs in the spring semester of 2020 facing the imminent pandemic (Study 1), investigate the consequence of those remote labs via university students response (Study 2), and prescribe practical guidelines for future remote lab design (Study 3). The research field of Hankuk University (pseudonym) initiated and enabled this overall research.
A framework to understand URL as the locus where the components of laboratory sessions and e-learning intersect was suggested. The reasons for implementing laboratory or e-learning courses lie in the purpose of laboratory or the promises and requirements of e-learning. As instructional programs, laboratory and e-learning should consider how the content is delivered, interactions between learners promoted, and assessment and feedback are provided. And those three factors in both programs naturally correspond to each other. The COVID-19 situation made the two strands of educational tradition meet, interplay, and blended in the various URL courses that emerged in 2020. The characteristics of the URL courses in 2020 were shaped according to each teaching and learning context, which includes sociocultural factors. And the lessons from URL instructors and students in 2020 (Study 1 and 2) led the researcher to an extended understanding of blended learning for laboratory education (see 2.3.4) and raised the need for an instructional design (ID) model for URLs (see 2.5 and Study 3).
For laboratory in science education, the purpose of laboratory, hands-on versus minds-on debate, interaction in laboratory, and lab report writing and feedback were contemplated. For e-learning and effective teaching strategies, the promises and requirements of e-learning, media presentation, aspects of online interaction, and assessment and feedback in e-learning were deliberated. For (re-)emergence of remote laboratory, studies before and after the COVID-19 were reviewed, and its meaning was revisited. Particularly, understanding remote laboratory as extended blended learning was suggested, which first blends the hands-on and minds-on laboratory experiences and second laboratory experiences and learning spaces.
Further, the instructor agency framework in science education was utilized to interpret the adaptive behavior of university STEM instructors while implementing their remote lab courses. The sociocultural perspective on Korean science instructors agency elaborated the researchers horizon of interpretation in macro-, meso- and micro- level structures. Also, the notion of design and development research in educational technology assured the utility of an ID model that is adaptive and flexible, which includes rapid prototyping (RP) when eliciting the course module for external validation.
In Study 1, the researcher compared four general remote labs, each for physics, chemistry, biology, and earth science, that were previously similar, and two major course labs at Hankuk University. The emergence of URL phenomena was interpreted from a sociocultural perspective, focusing on the structure posed by the COVID-19 pandemic and the educational authorities and the agency of university instructors. The macro-level context of Korea, the meso-level context of Hankuk University, and the micro-level context of each URL were closely interconnected with each other and the university instructors agency. In the spring semester of 2020, instructors agency was strongly shaped by the multi-level structures. However, the implemented URL in each discipline became quite various due to the endeavor instructors put in. The university instructors concerns were about video materials, data characteristics, limited interactions between them and students, difficulties in evaluation, and what students could gain from the URLs without hands-on experience. Since the fall semester of 2020, instructors have adapted to the situation, revised their URLs, and suggested further improvements. Study 1 reveals that university instructors agency led to the emergence of various remote laboratory course implementations in the context of an imminent emergency.
In Study 2, in step with Study 1, the researcher investigated how Hankuk University students perceived various remote laboratory course experiences in different content disciplines. Conducted as a mixed-methods study, online survey responses were collected from 338 students, and in-depth interviews were conducted with 18 students. ANOVA and Bonferroni post hoc tests of survey responses found that students perceptions of their URL experiences were significantly different (p < .05) dependent on content discipline (physics, chemistry, biology, earth science, and other majors). In addition, student interviews revealed that these differences in perceptions resulted from the different emergent teaching strategies used in each course. Suggestions were made for clearly setting learning objectives, carefully designing videos of experiments, offering collaborative synchronous online sessions, providing guidance and feedback for lab report writing, and introducing supportive assessments as strategies for future implementation of remote labs.
In Study 3, the BLEND (Blended Laboratory and E-learning iNstructional Design) ID model for URL was developed and validated. To respond to the fluctuating instructional environment of the pandemic, an ID model was promptly constructed and applied in the authentic learning context, iteratively revising the model with participant feedback. The research context was an Analytical Chemistry Experiment (ACE) course for pre-service chemistry teachers. The initial BLEND model was based on a literature review and lessons from Study 1 and 2 in 2020. For internal validation, six stakeholders participated in the usability test, and 10 subject-matter experts from various science disciplines and three educational technology experts provided expert reviews. For external validation, the URL course module was developed and implemented from the ID model, and seven university students who took the course responded to online surveys and participated in follow-up interviews. After two rounds of validation, the BLEND model was confirmed to be internally efficient and externally effective. The interactions with the instructor and peers, in particular, were highly appreciated. The finalized BLEND model for URL emphasizes constant formative evaluation and feedback and structures and visualizes the URL instructional system at both the weekly and overall course levels. Study 3 is a rare case of applying a design and development research method to science education.
Some issues were not resolved in this study and need follow-up research: (1) The interplay between the requirements of remote lab format and the nature of each science discipline (i.e., physics, chemistry, biology, and earth science) should be scrutinized. (2) How the experiment video should be designed, shot, and edited remains crucial. (3) An ID model for open-ended inquiry laboratory is a plausible future research topic. Then, how to evaluate the open-ended inquiry module arises as an essential prerequisite, which is also an important research agenda.
The strength of this study lies in its unique research field - Hankuk University in 2020 and 2021. This study seems to have collected extensive data for various remote lab courses that emerged in the initial situation of the COVID-19. Therefore, Study 1 to Study 3 can be said the attempts that report the URL phenomena during the early stage of the COVID-19 comprehensively. However, ironically, the COVID-19 situation that shaped the strength of this study can also be a double-edged sword as time passes and the situation changes. Consequently, the status of remote teachings, especially of remote labs in the post-COVID-19 era, is hard to predict.
If we take an optimistic view, our experience of URLs will broaden our imagination to evolve our laboratory education towards a blended format incorporating various learning modes across time and space. Indeed, the extended understanding of the blended learning for laboratory courses could shed some light on the path that overcoming the old dichotomies such as hands-on versus minds-on, synchronous vs. asynchronous, physical versus virtual, and place-based versus remote, to proceed toward better laboratory education.
In contrast, if we take a pessimistic view, we can expect that even our serious contemplation on remote labs may disappear someday, as many teaching methods did in the history of education. Therefore, it is recommended to recall fundamental questions on the essence of laboratory sessions that are rediscovered while we experience remote labs due to the COVID-19 (Q1-Q5). The easiest way to answer those questions would be by relying on the peculiarity of the learning objectives in each laboratory course - however, it does not open the way to more profound contemplations toward the post-COVID-19 laboratory education.
Instead, more certain answers for the abovementioned questions (Q1-Q5) could be meaningfully derived from participants' voices throughout this study: (A1) The minimum firsthand experience should be secured to foster students experimentation skills and provide students chances to engage with unexpected phenomena relevant to tacit knowledge and the nature of science. Note that a blended learning format can be an alternative that provides students with both hands-on and minds-on experiences. (A2) Instructors and students must have synchronous interactions in a temporal aspect. However, whether the spatial co-presence is necessary is not so manifest. (A3) If possible, a semester-long open-ended laboratory class would be the best chance to invite students to in-depth inquiry thinking. However, the gap between the theoretical prediction and the real experimental data seems to be the plausible locus where an inquiry may arise for cookbook-style labs in a practical sense. Therefore, the pre-lab activity, the characteristics of data, and peer discussions should be designed carefully. (A4) If the culture surrounding the laboratory education site favors the hand or mind as a cognitive channel or shapes the interaction between instructors and students vertically or horizontally, the answer would be yes. (A5) The notion of formative assessment of the instructional system may help make the laboratory courses more adaptive and flexible in various instructional situations, as in the BLEND model developed in Study 3.
The instructors and students at Hankuk University in 2020 were genuine agents who struggled to implement and take URL courses. And their lessons enabled the development of the BLEND model and the contemplation of the essence of laboratory sessions toward the post-COVID-19 laboratory education.Chapter 1. Introduction 1
1.1 Study Background 1
1.2 Purpose of Research 5
1.3 Research field 7
1.3.1 The Republic of Korea in the COVID-19 situation 8
1.3.2 Hankuk University in the Republic of Korea 9
1.4 Study Design 10
1.4.1 Study 1 11
1.4.2 Study 2 11
1.4.3 Study 3 12
Chapter 2. Theoretical Framework 13
2.1 Laboratory in Science Education 15
2.1.1 The purpose of laboratory 15
2.1.2 Hands-on versus minds-on debate 17
2.1.3 Interaction in laboratory 20
2.1.4 Laboratory report writing and feedback 21
2.2 E-learning and Effective Teaching Strategies 22
2.2.1 The promises and requirements of e-learning 22
2.2.2 Media presentation 24
2.2.3 Aspects of online interaction 25
2.2.4 Assessment and feedback 26
2.3 (Re-)emergence of Remote Laboratory 27
2.3.1 Studies on remote laboratories before the COVID-19 27
2.3.2 Studies on remote laboratories after the COVID-19 29
2.3.3 The meaning of remote laboratory revisited 31
2.3.4 Remote laboratory as blended learning 34
2.4 Instructor Agency and Sociocultural Perspective 38
2.4.1 Instructor agency in science education 38
2.4.2 Sociocultural perspective on Korean science instructors' agency 39
2.5 Design and Development Research 42
2.5.1 Utility of instructional design model 42
2.5.2 The need for a flexible model 43
2.5.3 Model development and validation research 44
2.5.4 Rapid prototyping approach 45
Chapter 3. Study 1: University Instructors' Agency During the Implementation of Remote Laboratory 46
3.1 Research Questions 47
3.2 Method 48
3.2.1 Participants 48
3.2.2 Qualitative interviews 49
3.2.3 Data analysis 50
3.3 Results 51
3.3.1 Macro-level context: South Korea 52
3.3.2 Meso-level context: Hankuk University and previous practices in laboratory courses 54
3.3.3 Micro-level context: Remote laboratories according to science discipline 56
3.3.4 The remote laboratories implemented at Hankuk University in the spring semester of 2020 60
3.3.5 Issues raised during the implementation of remote laboratories 64
3.3.6 University instructors' perceptions of the learning outcomes of remote laboratories 67
3.3.7
Teaching and Learning Tools for Introductory Programming in University Courses
Difficulties in teaching and learning introductory
programming have been studied over the years. The students'
difficulties lead to failure, lack of motivation, and abandonment
of courses. The problem is more significant in computer courses,
where learning programming is essential. Programming is
difficult and requires a lot of work from teachers and students.
Programming is a process of transforming a mental plan into a
computer program. The main goal of teaching programming is
for students to develop their skills to create computer programs
that solve real problems. There are several factors that can be
at the origin of the problem, such as the abstract concepts that
programming implies; the skills needed to solve problems; the
mental skills needed to decompose problems; many of the
students never had the opportunity to practice computational
thinking or programming; students must know the syntax,
semantics, and structure of a new unnatural language in a short
period of time. In this work, we present a set of strategies,
included in an application, with the objective of helping teachers
and students. Early identification of potential problems and
prompt response is critical to preventing student failure and
reducing dropout rates. This work also describes a predictive
machine learning (neural network) model of student failure
based on the student profile, which is built over the course of
programming lessons by continuously monitoring and
evaluating student activities
Recommended from our members
OULDI-JISC Project Evaluation Report: the impact of new curriculum design tools and approaches on institutional process and design cultures
This report presents research and evaluation undertaken by the OULDI-JISC Project (Open University Learning Design Initiative JISC Project) between 2008 and 2012. In particular, it considers the impact of new curriculum design tools and approaches piloted by the project on institutional processes and design cultures. These tools and approaches include tools for sharing learning design expertise (Cloudworks), visualising designs (CompendiumLD, Module Map, Activity Profile) and for supporting design and reflection in workshops (Facilitation Cards, workshop activities, etc.). The project has adopted a learning design approach so as to help foreground pedagogy and learner experience. Nine pilots have been completed across six UK universities
Recommended from our members
Developing sustainable business models for institutionsβ provision of open educational resources: Learning from OpenLearn usersβ motivations and experiences
Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OERβs value in generating revenue and detractors of OER have questioned whether they are in competition with formal education.
This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open Universityβs (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learnersβ performance and self-reliance, leading to increased retention and satisfaction with the learning experience
Engagement in a virtual learning environment predicts academic achievement in research methods modules: A longitudinal study combining behavioral and self-reported data
The use of virtual learning environments (VLE) has grown exponentially in the past years. Research indicates that studentsβ online learning behavior predicts their academic performance and that studentsβ academic emotions can play a key role in this process. However, few studies have attempted to investigate the effectiveness of VLE activities in learning achievement within psychology education. In this longitudinal study, we analyzed the relationship between studentsβ activity in a VLE, their attendance, academic emotions, and module grades at a face-to-face-based university in the United Kingdom. Data were collected over 1 year across two research methods modules, each of which is compulsory for a psychology degree. VLE and attendance data from 210 students were gathered for the first-year module, with 152 students continuing to the second year. The data were cross-referenced with studentsβ module grades, alongside self-reported emotion data for a subset of students. The results showed that overall VLE activity and the use of specific online tools such as optional online tests and lecture recording were important predictors of academic achievement. While some significant relationships between emotions and studentβs learning behavior and achievement were found, these correlations were relatively small and not consistent throughout the year. These findings have potential implications for curriculum design, particularly by making psychology educators aware of the usefulness of VLE activities and tools from the onset of studentsβ research methods learning journey
Recommended from our members
Open educational resources for all? Comparing user motivations and characteristics across The Open Universityβs iTunes U channel and OpenLearn platform.
With the rise in access to mobile multimedia devices, educational institutions have exploited the iTunes U platform as an additional channel to provide free educational resources with the aim of profile-raising and breaking down barriers to education. For those prepared to invest in content preparation, it is possible to produce interactive, portable material that can be made available globally. Commentators have questioned both the financial implications for platform-specific content production, and the availability of devices for learners to access it (Osborne, 2012).
The Open University (OU) makes its free educational resources available on iTunes U and via its web-based open educational resources (OER) platform, OpenLearn. The OUβs OER on iTunes U reached the 60 million download mark in 2013; its OpenLearn platform boasts 27 million unique visitors since 2006. This paper reports the results of a large-scale study of users of the OUβs iTunes U channel and OpenLearn platform. A survey of several thousand users revealed key differences in demographics between those accessing OER via the web and via iTunes U. In addition, the data allowed comparison between three groups: formal learners, informal learners and educators.
The study raises questions about whether university-provided OER meet the needs of users and makes recommendations for how content can be modified to suit their needs. As the publishing of OER becomes core to business, we reflect on reasons why understanding usersβ motivations and demographics is vital, allowing for needs-led resource provision and content that is adapted to best achieve learner satisfaction, and to deliver institutionsβ social mission
- β¦