4,402 research outputs found

    Pengembangan Modul Statistika Deskriptif Berbasis Penalaran Statistik

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    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.&nbsp

    Variability in University Students’ Use of Technology: An 'Approaches to Learning' Perspective

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    This study reports the results of a cross-case study analysis of how students’ 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

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μ‚¬λ²”λŒ€ν•™ κ³Όν•™κ΅μœ‘κ³Ό(화학전곡), 2023. 2. ν™ν›ˆκΈ°.2020년에 λ°œμƒν•œ μ½”λ‘œλ‚˜-19 μ‚¬νƒœμ™€ 이둜 μΈν•œ μ‚¬νšŒμ  거리두기 λ°©μ—­ 정책은 λŒ€ν•™ μ‹€ν—˜ μˆ˜μ—…λ“€μ΄ κ΄€μŠ΅μ μΈ λŒ€λ©΄ λ°©μ‹μ—μ„œ μ΅μˆ™ν•˜μ§€ μ•Šμ€ λΉ„λŒ€λ©΄ λ°©μ‹μœΌλ‘œ κ°‘μž‘μŠ€λŸ½κ²Œ μ „ν™˜λ˜λŠ” 상황을 μ•ΌκΈ°ν•˜μ˜€λ‹€. μ½”λ‘œλ‚˜-19둜 μΈν•œ 세계적인 ꡐ윑 결손이 μ˜ˆμƒλ˜λŠ” μƒν™©μ—μ„œ, κ³Όν•™κ΅μœ‘ν•™μžλ“€μ€ λΉ„λŒ€λ©΄ 원격 μ‹€ν—˜ μˆ˜μ—…μ΄ κ°€μ Έμ˜¨ μ‹€ν—˜ ꡐ윑의 변화에 μ£Όλͺ©ν•˜λ©° κ·Έ μ „κ°œμ™€ 결과에 λŒ€ν•œ κ²½ν—˜μ μΈ 연ꡬλ₯Ό μ΄‰κ΅¬ν•˜μ˜€λ‹€. 이에 λ³Έ μ—°κ΅¬μžλŠ” λ‹€μŒκ³Ό 같은 두 가지 λͺ©ν‘œλ₯Ό μ§€λ‹ˆκ³  연ꡬλ₯Ό μˆ˜ν–‰ν•˜μ˜€λ‹€. 첫째, 원격 μ‹€ν—˜ μˆ˜μ—…μ΄λΌλŠ” 초유의 상황에 μ§λ©΄ν•˜μ—¬ 제기된 μ‹€ν—˜ ꡐ윑의 본질(essence)에 κ΄€ν•œ 근본적인 μ§ˆλ¬Έλ“€μ— λ‹΅ν•˜κ³ μž ν•œλ‹€. κ·ΈλŸ¬ν•œ μ§ˆλ¬Έλ“€μ€ λ‹€μŒκ³Ό 같이 μš”μ•½λ  수 μžˆμ„ 것이닀. (λ¬Έ 1) λŒ€ν•™μ€ λ¬Όλ‘  K-12 κ³Όν•™κ΅μœ‘μ— 이λ₯΄κΈ°κΉŒμ§€ μ‹€ν—˜ μˆ˜μ—… κ²½ν—˜μ˜ λ³Έμ§ˆμ€ 무엇인가? 만쑱슀러운 ν•™μŠ΅ κ²°κ³Όκ°€ μ–΄λŠ 정도 보μž₯λœλ‹€λ©΄ 원격 마인즈온 μˆ˜μ—…μ΄ ν•Έμ¦ˆμ˜¨ κ²½ν—˜μ„ λŒ€μ²΄ν•  수 μžˆλŠ”κ°€? (λ¬Έ 2) κ΅μˆ˜μžμ™€ ν•™μƒμ˜ μ‹œκ³΅κ°„μ  곡동-쑴재(co-presence)λŠ” ν•„μˆ˜μ μΈκ°€? (λ¬Έ 3) μš°λ¦¬λŠ” μ–΄λ–»κ²Œ 학생듀은 μžμ—° ν˜„μƒμ— λŒ€ν•œ νƒκ΅¬λ‘œ μ΄ˆλŒ€ν•˜κ³ , 그것을 μ‹€ν—˜ λ³΄κ³ μ„œμ—μ„œ 과학적 κΈ€μ“°κΈ°λ‘œμ„œ ν‘œν˜„ν•˜λ„λ‘ ν•  수 μžˆλŠ”κ°€? (λ¬Έ 4) μœ„μ— λŒ€ν•œ 닡은 μ„Έκ³„μ˜ μ—¬λŸ¬ λ¬Έν™” 및 그에 λ”°λ₯Έ κ΅μˆ˜μžμ™€ 학생 κ°„μ˜ μƒν˜Έμž‘μš©μ˜ νŠΉμ„±μ— 따라 λ‹¬λΌμ§€λŠ”κ°€? (λ¬Έ 5) μš°λ¦¬λŠ” μ–΄λ–»κ²Œ 일반적인 상황뿐 μ•„λ‹ˆλΌ κΈ΄κΈ‰ν•œ μƒν™©μ—μ„œλ„ μ‹€ν–‰ν•  수 μžˆλŠ” 효과적이고 적응적인 μ‹€ν—˜ μˆ˜μ—…μ„ 섀계할 수 μžˆλŠ”κ°€? 이에 λŒ€ν•œ μž μ •μ μΈ 닡을 μ—°κ΅¬μ˜ 이둠적 ν‹€κ³Ό ν•¨κ»˜ μ‚΄νŽ΄λ³΄κ³ , 보닀 직접적인 닡을 μ—°κ΅¬μ˜ 결과에 λΉ„μΆ˜ λ…Όμ˜μ—μ„œ μ œμ‹œν•˜κ³ μž ν•˜μ˜€λ‹€. λ‘˜μ§Έ, λ³Έ 논문은 2020년에 μ½”λ‘œλ‚˜-19둜 μΈν•˜μ—¬ μ΄‰λ°œλœ 원격 μ‹€ν—˜ μˆ˜μ—…μ— κ΄€ν•˜μ—¬ λŒ€ν•™μ—μ„œμ˜ 이곡계열 κ΅μœ‘μ— μ–΄λ– ν•œ ν˜„μƒμ΄ λ°œμƒν•˜μ˜€λŠ”μ§€λ₯Ό μ‘°μ‚¬ν•˜κ³  ν–₯ν›„μ˜ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μœ„ν•œ μ‹€μ œμ μΈ ν•¨μ˜λ₯Ό μ œκ³΅ν•˜λŠ” 일을 λͺ©ν‘œλ‘œ ν•˜μ˜€λ‹€. 보닀 ꡬ체적으둜, λ³Έ 논문은 λŒ€ν•™ κ΅μˆ˜μžλ“€μ΄ 2020λ…„ 봄학기에 νŒ¬λ°λ―Ήμ„ μ§λ©΄ν•˜μ—¬ μ–΄λ–»κ²Œ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μ‹€ν–‰(implement)ν•˜μ˜€λŠ”μ§€λ₯Ό ν•©λ¦¬μ μœΌλ‘œ μ„€λͺ…ν•˜κ³ (연ꡬ 1), ν•™μƒλ“€μ˜ λ°˜μ‘μ„ 톡해 κ·Έ 원격 μ‹€ν—˜ μˆ˜μ—…μ˜ κ²°κ³Όλ₯Ό μ‘°μ‚¬ν•˜λ©°(연ꡬ 3), 미래의 λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—… 섀계λ₯Ό μœ„ν•œ μ‹€μ œμ μΈ 지침(guideline)을 μ œκ³΅ν•˜κ³ μž ν•˜μ˜€λ‹€. λ³Έ μ—°κ΅¬μ˜ ν˜„μž₯인 ν•œκ΅­λŒ€ν•™κ΅(κ°€λͺ…)의 상황이 μ΄λŸ¬ν•œ μ „λ°˜μ μΈ μ—°κ΅¬μ˜ μ‹œμž‘κ³Ό μˆ˜ν–‰μ„ κ°€λŠ₯ν•˜κ²Œ ν•˜μ˜€λ‹€. 이둠적 ν‹€λ‘œμ„œ, λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μ‹€ν—˜ μˆ˜μ—…κ³Ό μ΄λŸ¬λ‹(e-learning)의 각 μš”μ†Œκ°€ κ΅μ°¨ν•˜λŠ” μ§€μ μœΌλ‘œ μ΄ν•΄ν•˜λŠ” 관점을 μ œμ•ˆν•˜μ˜€λ‹€. μš°μ„ , μ‹€ν—˜ μˆ˜μ—… λ˜λŠ” μ΄λŸ¬λ‹ μˆ˜μ—…μ„ μ‹€ν–‰ν•˜λŠ” μ΄μœ λŠ” μ‹€ν—˜ μˆ˜μ—…μ˜ λͺ©μ  λ˜λŠ” μ΄λŸ¬λ‹μ˜ κ°€λŠ₯μ„± 및 μš”κ΅¬μ— 놓여 μžˆλ‹€. ꡐ수 ν”„λ‘œκ·Έλž¨μ˜ μΌμ’…μœΌλ‘œμ„œ, μ‹€ν—˜ μˆ˜μ—…κ³Ό μ΄λŸ¬λ‹μ€ μ–΄λ–»κ²Œ λ‚΄μš©μ„ μ „λ‹¬ν•˜κ³ , ν•™μŠ΅μž κ°„ μƒν˜Έμž‘μš©μ„ μ΄‰μ§„ν•˜κ³ , 평가와 ν”Όλ“œλ°±μ„ μ œκ³΅ν•˜λŠ”μ§€λ₯Ό κ³ λ €ν•΄μ•Όλ§Œ ν•œλ‹€. 그리고 두 ν”„λ‘œκ·Έλž¨λ“€μ—μ„œ μ΄λŸ¬ν•œ μ„Έ μš”μ†Œλ“€μ€ μ„œλ‘œ μžμ—°μŠ€λŸ½κ²Œ λŒ€μ‘ν•œλ‹€. 2020λ…„μ˜ λ‹€μ–‘ν•œ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…λ“€μ€ μ½”λ‘œλ‚˜-19 μƒν™©μ—μ„œ μ΄λŸ¬ν•œ 두 ꡐ윑적 전톡이 λ§Œλ‚˜μ„œ, κ΅ν˜Έν•˜λ©°, ν˜Όν•©λœ(blended) μ§€μ μ΄μ—ˆλ‹€. λ˜ν•œ 2020λ…„μ˜ λ‹€μ–‘ν•œ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…λ“€μ˜ νŠΉμ„±μ€ μ‚¬νšŒλ¬Έν™”μ μΈ μš”μ†Œλ₯Ό ν¬ν•¨ν•˜λŠ” 각각의 κ΅μˆ˜ν•™μŠ΅ λ§₯λ½μ—μ„œ ν˜•μ„±λ˜μ—ˆλ‹€. 2020λ…„μ˜ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—… ꡐ수자 및 ν•™μƒλ“€λ‘œλΆ€ν„° 얻은 κ΅ν›ˆμ€(연ꡬ 1 및 2) λ³Έ μ—°κ΅¬μžκ°€ μ‹€ν—˜ κ΅μœ‘μ„ μœ„ν•˜μ—¬ ν™•μž₯된 λΈ”λ Œλ””λ“œ(blended) λŸ¬λ‹ 이해에 λ„λ‹¬ν•˜κ²Œ ν•˜μ˜€μœΌλ©°(2.3.4 μ°Έμ‘°) λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μœ„ν•œ ꡐ수 섀계(instructional design) λͺ¨ν˜•μ˜ ν•„μš”μ„± μ—­μ‹œ μ œκΈ°ν•˜μ˜€λ‹€. κ³Όν•™κ΅μœ‘μ—μ„œμ˜ μ‹€ν—˜ μˆ˜μ—…μ— κ΄€ν•˜μ—¬, μ‹€ν—˜ μˆ˜μ—…μ˜ λͺ©μ κ³Ό, ν•Έμ¦ˆμ˜¨(hands-on) 및 마인즈온(minds-on) λ…ΌμŸκ³Ό, μ‹€ν—˜ λ³΄κ³ μ„œ μ“°κΈ° 및 ν”Όλ“œλ°± 방법을 κ³ μ°°ν•˜μ˜€λ‹€. μ΄λŸ¬λ‹ 및 효과적인 ꡐ수 μ „λž΅μ— κ΄€ν•˜μ—¬, μ΄λŸ¬λ‹μ˜ 전망 및 μš”κ΅¬μ™€, 맀체(media) μ œμ‹œμ™€, 온라인 μƒν˜Έμž‘μš©μ˜ 양상과, μ΄λŸ¬λ‹μ—μ„œμ˜ 평가 및 ν”Όλ“œλ°±μ„ μˆ™κ³ ν•˜μ˜€λ‹€. 원격 μ‹€ν—˜ μˆ˜μ—…μ˜ (재)μ°½λ°œμ— κ΄€ν•˜μ—¬λŠ” μ½”λ‘œλ‚˜-19 이전과 μ΄ν›„μ˜ 연ꡬ듀을 λŒμ•„λ³΄κ³ , ν•΄λ‹Ή μš©μ–΄μ˜ 의미λ₯Ό λ„μΆœν•˜μ˜€λ‹€. νŠΉλ³„νžˆ, 원격 μ‹€ν—˜ μˆ˜μ—…μ„ ν™•μž₯된 λΈ”λ Œλ””λ“œ λŸ¬λ‹μœΌλ‘œ μ΄ν•΄ν•˜λŠ” 관점을 μ œμ•ˆν•˜μ˜€λŠ”λ°, μ΄λŠ” 첫째둜 ν•Έμ¦ˆμ˜¨ 및 마인즈온 μ‹€ν—˜ κ²½ν—˜μ„ ν˜Όν•©ν•˜κ³  λ‘˜μ§Έλ‘œ μ‹€ν—˜ κ²½ν—˜λ“€κ³Ό ν•™μŠ΅ 곡간듀을 ν˜Όν•©ν•˜λŠ” κ²ƒμ΄μ—ˆλ‹€. λ”ν•˜μ—¬, κ³Όν•™κ΅μœ‘μ—μ„œμ˜ ꡐ수자 ν–‰μœ„μ£Όμ²΄μ„±(agency)을 ν™œμš©ν•˜μ—¬ λŒ€ν•™μ˜ 이곡계열 κ΅μˆ˜μžλ“€μ΄ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μ‹€ν–‰ν•  λ•Œμ˜ 적응적인 행동을 ν•΄μ„ν•˜μ˜€λ‹€. μš°λ¦¬λ‚˜λΌ κ³Όν•™ κ΅μˆ˜μžλ“€μ˜ ν–‰μœ„μ£Όμ²΄μ„±μ— λŒ€ν•œ μ‚¬νšŒλ¬Έν™”μ  μ‹œκ°μ€ μ—°κ΅¬μžμ˜ ν•΄μ„μ˜ 지평을 κ±°μ‹œμ (macro-), μ€‘μ‹œμ (meso-), 그리고 λ―Έμ‹œμ (micro-) μˆ˜μ€€μ˜ ꡬ쑰(structure)λ“€λ‘œ μ •κ΅ν™”ν•˜μ˜€λ‹€. λ˜ν•œ, κ΅μœ‘κ³΅ν•™ λΆ„μ•Όμ—μ„œμ˜ 섀계 및 개발 연ꡬ 관점에 따라 μœ μ—°ν•˜κ³ (flexible) 반볡적인(iterative) ꡐ수 섀계 λͺ¨ν˜•μ˜ μœ μš©μ„±μ„ μ œμ•ˆν•˜μ˜€μœΌλ©°, μ΄λŠ” 외적 타당화λ₯Ό μœ„ν•œ μˆ˜μ—… λͺ¨λ“ˆ λ„μΆœ κ³Όμ •μ—μ„œμ˜ λž˜ν”Όλ“œ ν”„λ‘œν† νƒ€μ΄ν•‘(rapid prototyping)을 ν¬ν•¨ν•˜λŠ” κ²ƒμ΄μ—ˆλ‹€. 연ꡬ 1μ—μ„œ, μ—°κ΅¬μžλŠ” ν•œκ΅­λŒ€ν•™κ΅μ—μ„œ μ½”λ‘œλ‚˜-19 이전에 μ„œλ‘œ λΉ„μŠ·ν•˜μ˜€λ˜ 일반 물리학, ν™”ν•™, 생물학, 지ꡬ과학 μ‹€ν—˜λΏλ§Œ μ•„λ‹ˆλΌ 2개의 전곡 ꡐ과 μ‹€ν—˜ μˆ˜μ—…μ„ λΉ„κ΅ν•˜μ˜€λ‹€. μ—°κ΅¬μžλŠ” λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—… ν˜„μƒμ˜ μ°½λ°œμ„ μ‚¬νšŒλ¬Έν™”μ  κ΄€μ μ—μ„œ ν•΄μ„ν•˜μ˜€λŠ”λ°, 이 λ•Œ μ½”λ‘œλ‚˜-19 팬데믹과 ꡐ윑 당ꡭ에 μ˜ν•˜μ—¬ λΆ€κ³Όλœ ꡬ쑰 및 λŒ€ν•™ κ΅μˆ˜μžλ“€μ˜ ν–‰μœ„μ£Όμ²΄μ„±μ— μ£Όλͺ©ν•˜μ˜€λ‹€. κ±°μ‹œμ  μˆ˜μ€€μ˜ ν•œκ΅­ λ§₯락, μ€‘μ‹œμ  μˆ˜μ€€μ˜ ν•œκ΅­λŒ€ν•™κ΅ λ§₯락, 그리고 λ―Έμ‹œμ  μˆ˜μ€€μ˜ κ°œλ³„ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—… λ§₯락은 μ„œλ‘œ 뿐만 μ•„λ‹ˆλΌ λŒ€ν•™ ꡐ수자의 ν–‰μœ„μ£Όμ²΄μ„±κ³Όλ„ λ°€μ ‘ν•˜κ²Œ μƒν˜Έμ—°κ΄€λ˜μ–΄ μžˆμ—ˆλ‹€. 2020λ…„ 봄학기에, ꡐ수자의 ν–‰μœ„μ£Όμ²΄μ„±μ€ μ΄λŸ¬ν•œ 닀측적(multi-level) ꡬ쑰듀에 μ˜ν•˜μ—¬ λͺ¨μ–‘μ§€μ–΄μ‘Œλ‹€(shaped). κ·ΈλŸ¬λ‚˜, κ°œλ³„ ꡐ과(discipline)에 따라 μ‹€ν–‰λœ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ€ κ΅μˆ˜μžκ°€ νˆ¬μž…ν•œ λ…Έλ ₯에 따라 μƒλ‹Ήνžˆ λ‹€μ–‘ν•˜κ²Œ λ˜μ—ˆλ‹€. λŒ€ν•™ κ΅μˆ˜μžλ“€μ˜ 고렀사항은 λ™μ˜μƒ 자료, μ‹€ν—˜ λ°μ΄ν„°μ˜ νŠΉμ„±, μžμ‹ λ“€κ³Ό 학생듀 κ°„μ˜ μ œν•œλœ μƒν˜Έμž‘μš©, ν‰κ°€μ˜ 어렀움, 그리고 학생듀이 ν•Έμ¦ˆμ˜¨ κ²½ν—˜μ΄ 없이 원격 μ‹€ν—˜ μˆ˜μ—…μ—μ„œ 무엇을 얻을(gain) 수 μžˆλŠ”κ°€ ν•˜λŠ” μ μ΄μ—ˆλ‹€. 2020λ…„ 가을학기뢀터 λŒ€ν•™ κ΅μˆ˜μžλ“€μ€ 상황에 μ μ‘ν•˜μ—¬ μžμ‹ λ“€μ˜ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ κ°œμ„ ν•˜μ˜€μœΌλ©°, 더 λ§Žμ€ κ°œμ„ μ λ“€μ„ μ œμ•ˆν•˜μ˜€λ‹€. 연ꡬ 1의 κ²°κ³ΌλŠ” λŒ€ν•™ ꡐ수자의 ν–‰μœ„μ£Όμ²΄μ„±μ΄ μž„λ°•ν•œ κΈ΄κΈ‰ μƒν™©μ—μ„œ λ‹€μ–‘ν•œ 원격 μ‹€ν—˜ μˆ˜μ—… 싀행이 μ°½λ°œν•˜λŠ” κ²°κ³Όλ₯Ό λ‚³μ•˜μŒμ„ 보여쀀닀. 연ꡬ 2λŠ” 연ꡬ 1κ³Ό λ°œλ§žμΆ”μ–΄ ν•œκ΅­λŒ€ν•™κ΅μ—μ„œ μˆ˜ν–‰λ˜μ—ˆλ‹€. μ—°κ΅¬μžλŠ” λŒ€ν•™μƒλ“€μ΄ μ„œλ‘œ λ‹€λ₯Έ ꡐ과의 λ‹€μ–‘ν•œ 원격 μ‹€ν—˜ μˆ˜μ—… κ²½ν—˜μ„ μ–΄λ–»κ²Œ μΈμ‹ν•˜μ˜€λŠ”μ§€λ₯Ό μ‘°μ‚¬ν•˜μ˜€λ‹€. 연ꡬ 2λŠ” ν˜Όν•© μ—°κ΅¬λ‘œμ„œ, 338λͺ…μ˜ ν•™μƒλ“€λ‘œλΆ€ν„° 온라인 μ„€λ¬Έ 응닡을 μ–»μ—ˆμœΌλ©° 18λͺ…μ˜ 학생듀과 인터뷰λ₯Ό μ‹€μ‹œν•˜μ˜€λ‹€. 뢄산뢄석(ANOVA)κ³Ό Bonferroni 사후 검정을 톡해 원격 μ‹€ν—˜ μˆ˜μ—… κ²½ν—˜μ— λŒ€ν•œ ν•™μƒλ“€μ˜ 인식이 ꡐ과(물리, ν™”ν•™, 생물, 지ꡬ과학, λ‹€λ₯Έ 전곡 κ³Όλͺ©)에 따라 ν†΅κ³„μ μœΌλ‘œ μœ μ˜λ―Έν•˜κ²Œ λ‹€λ₯΄λ‹€λŠ” 점을 λ°œκ²¬ν•˜μ˜€λ‹€(p < .05). λ”ν•˜μ—¬, 학생 μΈν„°λ·°λŠ” μ΄λŸ¬ν•œ 차이듀이 κ°œλ³„ ꡐ과λͺ©μ—μ„œ μ°½λ°œν•œ ꡐ수 μ „λž΅μ— μ˜ν•˜μ—¬ λ°œμƒν•˜μ˜€μŒμ„ λ“œλŸ¬λ‚΄μ—ˆλ‹€. ν–₯ν›„μ˜ 효과적인 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μœ„ν•œ μ „λž΅μœΌλ‘œμ„œ, μˆ˜μ—…μ˜ λͺ©μ μ„ λͺ…ν™•νžˆ μ„€μ •ν•˜κΈ°, μ‹€ν—˜ λ™μ˜μƒμ„ μ„Έμ‹¬ν•˜κ²Œ μ„€κ³„ν•˜κΈ°, λ™μ‹œμ (synchronous) 온라인 ν˜‘λ ₯ μ„Έμ…˜ μ œκ³΅ν•˜κΈ°, μ‹€ν—˜ λ³΄κ³ μ„œ μž‘μ„±μ— λŒ€ν•œ ν”Όλ“œλ°±μ„ μ œκ³΅ν•˜κ³  보좩적 평가λ₯Ό μ‹€μ‹œν•˜κΈ° 등을 μ œμ•ˆν•˜μ˜€λ‹€. 연ꡬ 3μ—μ„œ μ—°κ΅¬μžλŠ” λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μœ„ν•œ λΈ”λ Œλ””λ“œ μ‹€ν—˜ 및 μ΄λŸ¬λ‹ ꡐ수 섀계(Blended Laboratory and E-learning iNstructional Design, BLEND) λͺ¨ν˜•μ„ κ°œλ°œν•˜κ³  νƒ€λ‹Ήν™”ν•˜μ˜€λ‹€. νŒ¬λ°λ―Ήμ— μ˜ν•˜μ—¬ μš”λ™ν•˜λŠ” ꡐ수 ν™˜κ²½μ— λŒ€μ‘ν•˜κΈ° μœ„ν•΄, μ—°κ΅¬μžλŠ” ꡐ수 섀계 λͺ¨ν˜•μ„ μ‹ μ†ν•˜κ²Œ κ΅¬μΆ•ν•˜μ—¬ μ‹€μ œμ  ν•™μŠ΅ λ§₯락에 μ μš©ν•˜κ³ , μ°Έμ—¬μžμ˜ ν”Όλ“œλ°±μ„ ν†΅ν•œ 반볡적(iterative) λͺ¨ν˜• μˆ˜μ •μ„ μ‹œλ„ν•˜μ˜€λ‹€. 연ꡬ λ§₯락은 μ˜ˆλΉ„ ν™”ν•™ ꡐ사듀을 μœ„ν•œ λΆ„μ„ν™”ν•™μ‹€ν—˜ κ°•μ’Œμ˜€λ‹€. 초기 BLEND λͺ¨ν˜•μ€ λ¬Έν—Œ 리뷰 및 2020λ…„μ˜ 연ꡬ 1κ³Ό 연ꡬ 2의 κ΅ν›ˆμ— κΈ°λ°˜ν•˜μ—¬ λ„μΆœλ˜μ—ˆλ‹€. 내적(internal) 타당화λ₯Ό μœ„ν•΄ 6λͺ…μ˜ μ΄ν•΄λ‹Ήμ‚¬μž(stakeholder)κ°€ μ‚¬μš©μ„± 평가(usability test)에 μ°Έμ—¬ν•˜μ˜€μœΌλ©°, λ‹€μ–‘ν•œ κ³Όν•™ ꡐ과 배경의 10λͺ…μ˜ λ‚΄μš© 전문가와 3λͺ…μ˜ κ΅μœ‘κ³΅ν•™ μ „λ¬Έκ°€κ°€ μ „λ¬Έκ°€ 리뷰λ₯Ό μ œκ³΅ν•˜μ˜€λ‹€. 외적(external) 타당화λ₯Ό μœ„ν•΄ ν•΄λ‹Ή μ‹œκΈ°μ˜ ꡐ수 섀계 λͺ¨ν˜•μ„ 기반으둜 λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—… λͺ¨λ“ˆμ΄ 개발 및 μ‹€ν–‰λ˜μ—ˆκ³ , ν•΄λ‹Ή κ°•μ’Œλ₯Ό μˆ˜κ°•ν•˜λŠ” 7λͺ…μ˜ λŒ€ν•™μƒλ“€μ΄ 온라인 μ„€λ¬Έ 및 후속 인터뷰에 μ°Έμ—¬ν•˜μ˜€λ‹€. 2회기의 타당화 과정을 거쳐, BLEND λͺ¨ν˜•μ€ λ‚΄μ μœΌλ‘œ 효율적이며(efficient) μ™Έμ μœΌλ‘œ 효과적(effective)인 κ²ƒμœΌλ‘œ νƒ€λ‹Ήν™”λ˜μ—ˆλ‹€. 이 λ•Œ ꡐ수자 및 학생 κ°„μ˜ 높은 μƒν˜Έμž‘μš©μ΄ νŠΉλ³„νžˆ μ£Όλͺ©λ˜μ—ˆλ‹€. λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μœ„ν•œ μ΅œμ’… BLEND λͺ¨ν˜•μ€ 지속적인 ν˜•μ„± 평가와 ν”Όλ“œλ°±μ„ μ€‘μ‹œν•˜λ©°, 주별 그리고 κ°•μ’Œλ³„ μˆ˜μ€€μ—μ„œμ˜ 원격 μ‹€ν—˜ μˆ˜μ—… ꡐ수 체제λ₯Ό κ΅¬μ‘°ν™”ν•˜κ³  μ‹œκ°ν™”ν•˜μ˜€λ‹€. 연ꡬ 3은 κ³Όν•™κ΅μœ‘μ—μ„œ 섀계 및 개발 연ꡬ 방법을 μ μš©ν•œ λ“œλ¬Έ 사둀이닀. λ³Έ μ—°κ΅¬μ—μ„œ λͺ¨λ‘ ν•΄κ²°λ˜μ§€ μ•Šκ³  μ—¬μ „νžˆ 후속 연ꡬλ₯Ό μš”κ΅¬ν•˜λŠ” μŸμ λ“€μ€ λ‹€μŒκ³Ό κ°™λ‹€: (1) 원격 μ‹€ν—˜ ν˜•μ‹μ΄ μš”κ΅¬ν•˜λŠ” 바와 각각의 κ³Όν•™ κ³Όλͺ©(물리, ν™”ν•™, 생물, 지ꡬ과학 λ“±)의 νŠΉμ„± μ‚¬μ΄μ˜ μƒν˜Έμž‘μš©μ΄ 더 μžμ„Ένžˆ κ³ μ°°λ˜μ–΄μ•Ό ν•œλ‹€. (2) μ‹€ν—˜ λ™μ˜μƒμ„ μ–΄λ–»κ²Œ μ„€κ³„ν•˜κ³ , μ΄¬μ˜ν•˜λ©°, νŽΈμ§‘ν•΄μ•Ό ν•˜λŠ”μ§€μ˜ λ¬Έμ œκ°€ μ—¬μ „νžˆ μ€‘μš”ν•˜λ‹€. (3) κ°œλ°©ν˜•(open-ended) 탐ꡬ μ‹€ν—˜ μˆ˜μ—…μ„ μœ„ν•œ ꡐ수 섀계 λͺ¨ν˜•μ΄ ν–₯ν›„μ˜ μ€‘μš”ν•œ 연ꡬ μ£Όμ œμ΄λ‹€. 이 경우, κ°œλ°©ν˜• 탐ꡬ μˆ˜μ—… ν”„λ‘œκ·Έλž¨μ„ μ–΄λ–»κ²Œ 평가할 것인지 μ—­μ‹œ λ°˜λ“œμ‹œ λ¨Όμ € ν•΄κ²°λ˜μ–΄μ•Ό ν•  연ꡬ μ£Όμ œκ°€ 될 것이닀. λ³Έ μ—°κ΅¬μ˜ 강점은 2020λ…„ 및 2021λ…„μ˜ ν•œκ΅­λŒ€ν•™κ΅λΌλŠ” 연ꡬ ν˜„μž₯의 λ…νŠΉμ„±μ— κΈ°μΈν•œλ‹€. λ³Έ μ—°κ΅¬λŠ” μ½”λ‘œλ‚˜-19 초기 μƒν™©μ—μ„œ μ°½λ°œν•œ 원격 μ‹€ν—˜ μˆ˜μ—…μ— κ΄€ν•˜μ—¬ μƒλ‹Ήνžˆ λ§Žμ€ 데이터λ₯Ό μˆ˜μ§‘ν•œ 연ꡬ μ‚¬λ‘€λ‘œ 보인닀. κ·ΈλŸ¬λ―€λ‘œ, 연ꡬ 1μ—μ„œ 연ꡬ 3에 이λ₯΄λŠ” μž‘μ—…μ€ μ½”λ‘œλ‚˜-19의 초기 λ‹¨κ³„μ—μ„œ λ‚˜νƒ€λ‚œ 원격 μ‹€ν—˜ μˆ˜μ—… ν˜„μƒμ„ ν¬κ΄„μ μœΌλ‘œ λ³΄κ³ ν•˜λ €λŠ” μ‹œλ„λΌκ³  ν•  수 μžˆλ‹€. ν•˜μ§€λ§Œ μ—­μ„€μ μœΌλ‘œ. λ³Έ μ—°κ΅¬μ˜ 강점을 λ§Œλ“€μ—ˆλ˜ μ½”λ‘œλ‚˜-19 상황은 μ‹œκ°„μ΄ μ§€λ‚˜κ³  상황이 변화함에 따라 μ–‘λ‚ μ˜ κ²€μœΌλ‘œ μž‘μš©ν•  수 μžˆλ‹€. 결과적으둜, 포슀트-μ½”λ‘œλ‚˜-19 μ‹œλŒ€μ— 원격 μˆ˜μ—…, 특히 원격 μ‹€ν—˜ μˆ˜μ—…μ˜ μ§€μœ„κ°€ 어떠할지λ₯Ό μ˜ˆμƒν•˜κΈ°λž€ 쉽지 μ•Šλ‹€. λ§Œμ•½ μš°λ¦¬κ°€ 낙관적인 μ‹œμ„ μ„ μ·¨ν•œλ‹€λ©΄, λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ— λŒ€ν•œ 우리의 κ²½ν—˜μ€ μ‹€ν—˜ κ΅μœ‘μ— λŒ€ν•œ 우리의 상상을 ν™•μž₯μ‹œμΌœ, μ‹œκ°„κ³Ό 곡간을 λ„˜λ‚˜λ“€λ©° λ‹€μ–‘ν•œ ν•™μŠ΅ 양상을 ν†΅ν•©ν•˜λŠ” λΈ”λ Œλ””λ“œν˜•μ‹μ„ ν–₯ν•΄ μ „μ§„ν•˜κ²Œ ν•  것이닀. μ‹€μ œλ‘œ, μ‹€ν—˜ κ΅μœ‘μ„ μœ„ν•΄ ν™•μž₯된 λΈ”λ Œλ””λ“œ λŸ¬λ‹ μ΄ν•΄λŠ” ν•Έμ¦ˆμ˜¨ λŒ€ 마인즈온, λ™μ‹œμ  λŒ€ λΉ„λ™μ‹œμ , ν˜„μž₯ λŒ€ 원격 λ“±μ˜ 였랜 이뢄법을 λ„˜μ–΄ 더 λ‚˜μ€ μ‹€ν—˜ ꡐ윑으둜 λ‚˜μ•„κ°€λŠ” 길을 λΉ„μΆ˜ 면이 μžˆλ‹€. μ΄μ™€λŠ” λ°˜λŒ€λ‘œ, λ§Œμ•½ μš°λ¦¬κ°€ 비관적인 μ‹œμ„ μ„ μ·¨ν•œλ‹€λ©΄, 원격 μ‹€ν—˜ μˆ˜μ—…μ— λŒ€ν•œ 우리의 μ‹¬κ°ν•œ κ³ μ°° μ—­μ‹œ μ–Έμ  κ°€ μ‚¬λΌμ§ˆ 수 있으며, μ΄λŠ” κ΅μœ‘μ‚¬μ—μ„œ λ§Žμ€ ꡐ수 방법듀이 κ·ΈλŸ¬ν–ˆλ˜ 것과 λ§ˆμ°¬κ°€μ§€μ΄λ‹€. κ·ΈλŸ¬λ―€λ‘œ, μƒκΈ°ν•˜μ˜€λ“― μ½”λ‘œλ‚˜-19둜 μΈν•˜μ—¬ μš°λ¦¬κ°€ κ²½ν—˜ν•œ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ 톡해 재발견된 μ‹€ν—˜ μˆ˜μ—…μ˜ λ³Έμ§ˆμ— κ΄€ν•œ 근본적인 μ§ˆλ¬Έλ“€(λ¬Έ 1-5)에 λ‹΅ν•˜λŠ” 일이 μš”μ²­λœλ‹€. μ—¬κΈ°μ„œ μ΄λŸ¬ν•œ μ§ˆλ¬Έλ“€μ— λ‹΅ν•˜λŠ” κ°€μž₯ νŽΈλ¦¬ν•œ 방법은 각 μ‹€ν—˜ μˆ˜μ—…μ—μ„œ μ •ν•˜λŠ” ν•™μŠ΅ λͺ©ν‘œμ˜ νŠΉμˆ˜μ„±μ— μ˜μ‘΄ν•˜λŠ” κ²ƒμ΄κ² μ§€λ§Œ, μ΄λŸ¬ν•œ λ‹¨μˆœν•œ 해결책은 포슀트-μ½”λ‘œλ‚˜-19 μ‹€ν—˜ κ΅μœ‘μ„ μœ„ν•œ 더 μ‹¬ν™”λœ 고찰둜 λ‚˜μ•„κ°€λŠ” 길을 열어쀄 수 μ—†λ‹€. κ·ΈλŸ¬λ―€λ‘œ, μœ„μ—μ„œ 제기된 5κ°€μ§€μ˜ μ§ˆλ¬Έλ“€μ— λŒ€ν•΄ λ³Έ μ—°κ΅¬μ˜ μ°Έμ—¬μžλ“€μ˜ λͺ©μ†Œλ¦¬λ‘œλΆ€ν„° 보닀 ꡬ체적인 닡을 ν•΄λ³΄λŠ” 일이 의미 μžˆμ„ 것이닀: (λ‹΅ 1) 학생듀이 μ‹€ν—˜ κΈ°λŠ₯(skill)을 함양할 뿐만 μ•„λ‹ˆλΌ μ˜ˆμƒν•˜μ§€ λͺ»ν–ˆλ˜ ν˜„μƒκ³Ό ν•¨κ»˜ 암묡적 지식(tacit knowledge) 및 κ³Όν•™μ˜ λ³Έμ„±(nature of science)을 직면할 기회λ₯Ό μ œκ³΅ν•˜κΈ° μœ„ν•˜μ—¬, ν•™μƒλ“€μ—κ²Œ μ΅œμ†ŒλΆˆκ°€κ²°μ˜ ν•Έμ¦ˆμ˜¨ κ²½ν—˜μ„ μ œκ³΅ν•΄μ•Ό ν•œλ‹€. λΈ”λ Œλ””λ“œ λŸ¬λ‹ ν˜•μ‹μ€ ν•Έμ¦ˆμ˜¨ κ²½ν—˜κ³Ό 마인즈온 κ²½ν—˜μ„ λͺ¨λ‘ κ°–κ²Œ ν•˜λŠ” λŒ€μ•ˆμ΄ 될 수 μžˆλ‹€. (λ‹΅ 2) κ΅μˆ˜μžμ™€ 학생듀은 μ‹œκ°„μ μΈ μΈ‘λ©΄μ—μ„œλŠ” λ°˜λ“œμ‹œ λ™μ‹œμ  μƒν˜Έμž‘μš©μ„ ν•΄μ•Όλ§Œ ν•œλ‹€. λ‹€λ§Œ, κ·Έ 듀이 κ³΅κ°„μ μœΌλ‘œ ν•¨κ»˜ μžˆλŠ” 일이 ν•„μˆ˜μ μΈμ§€λŠ” λͺ…ν™•ν•˜μ§€ μ•Šλ‹€. (λ‹΅ 3) λ§Œμ•½ κ°€λŠ₯ν•˜λ‹€λ©΄, ν•™κΈ° λ‹¨μœ„μ˜ κ°œλ°©ν˜• μ‹€ν—˜ μˆ˜μ—…μ„ μ§„ν–‰ν•˜λŠ” 것이 학생듀을 깊이 μžˆλŠ” 탐ꡬ적 μ‚¬κ³ λ‘œ μ΄ˆλŒ€ν•˜λŠ” κ°€μž₯ 쒋은 κΈ°νšŒκ°€ 될 것이닀. ν•˜μ§€λ§Œ, ν˜„μ‹€μ μœΌλ‘œ μš”λ¦¬μ±…(cookbook) ν˜•μ‹μ˜ μ‹€ν—˜ μˆ˜μ—…λ“€μ—μ„œλŠ” 이둠적 예츑과 μ‹€μ œ μ‹€ν—˜ 데이터 μ‚¬μ΄μ˜ κ°„κ·Ήλ§Œμ΄ 탐ꡬ가 μΌμ–΄λ‚˜κ²Œ λ˜λŠ” μœ μΌν•œ 지점일 수 μžˆλ‹€. κ·ΈλŸ¬λ―€λ‘œ, μ˜ˆλΉ„μ‹€ν—˜(pre-lab) ν™œλ™, 데이터 νŠΉμ„±, λ™λ£Œ ν† λ‘ (discussion)이 주의 깊게 μ„€κ³„λ˜μ–΄μ•Ό ν•œλ‹€. (λ‹΅ 4) λ§Œμ•½ μ‹€ν—˜ μˆ˜μ—… ν˜„μž₯을 λ‘˜λŸ¬μ‹Ό λ¬Έν™”κ°€ 인지적 κ²½λ‘œλ‘œμ„œμ˜ 손(hand) λ˜λŠ” 마음(mind)을 κ°•μ‘°ν•˜κ±°λ‚˜, κ΅μˆ˜μžμ™€ 학생 κ°„μ˜ μƒν˜Έμž‘μš©μ„ 수직적으둜 λ˜λŠ” μˆ˜ν‰μ μœΌλ‘œ λ§Œλ“ λ‹€λ©΄, κ·Έλ ‡λ‹€κ³  ν•  수 μžˆλ‹€. (λ‹΅ 5) ꡐ수 μ²΄μ œμ— λŒ€ν•œ ν˜•μ„± ν‰κ°€λΌλŠ” κ°œλ…μ΄ μ‹€ν—˜ μˆ˜μ—…μ„ 더 적응적이고(adaptive) μœ μ—°ν•˜κ²Œ λ§Œλ“œλŠ” 방법일 수 μžˆλŠ”λ°, 이것은 연ꡬ 3μ—μ„œ 개발된 BLEND λͺ¨ν˜•μ—μ„œ 잘 λ“œλŸ¬λ‚œλ‹€. 2020λ…„ ν•œκ΅­λŒ€ν•™κ΅μ˜ κ΅μˆ˜μžμ™€ ν•™μŠ΅μžλ“€μ€ λŒ€ν•™ 원격 μ‹€ν—˜ μˆ˜μ—…μ„ μ‹€ν–‰ν•˜κ³  μˆ˜κ°•ν•˜κΈ° μœ„ν•΄ λ…Έλ ₯ν•œ μ§„μ •ν•œ ν–‰μœ„μžλ“€(agents)μ΄μ—ˆλ‹€. 그리고 그듀이 남긴 κ΅ν›ˆμ΄μ•Όλ§λ‘œ 포슀트-μ½”λ‘œλ‚˜-19 μ‹€ν—˜ μˆ˜μ—…μ„ ν–₯ν•˜λŠ” BLEND λͺ¨ν˜•μ˜ 개발 및 μ‹€ν—˜ μˆ˜μ—…μ˜ λ³Έμ§ˆμ— κ΄€ν•œ 고찰을 κ°€λŠ₯ν•˜κ²Œ ν•˜μ˜€λ‹€.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

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    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

    Engagement in a virtual learning environment predicts academic achievement in research methods modules: A longitudinal study combining behavioral and self-reported data

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    The use of virtual learning environments (VLE) has grown exponentially in the past years. Research indicates that students’ online learning behavior predicts their academic performance and that students’ 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 students’ 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 students’ 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 student’s 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 students’ research methods learning journey
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