10 research outputs found

    Generalizability Analysis of Student Achievement Tests with Various Item Weights

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ΅μœ‘ν•™κ³Ό κ΅μœ‘ν•™μ „κ³΅, 2016. 8. λ°•ν˜„μ •.μš°λ¦¬λ‚˜λΌμ˜ μ€‘β€€κ³ λ“±ν•™κ΅μ—μ„œ 주둜 μ“°μ΄λŠ” κ²€μ‚¬λŠ” λ¬Έν•­ μœ ν˜•κ³Ό 배점의 λ‹€μ–‘ν™”λ₯Ό 톡해 κ²€μ‚¬μ˜ 변별λ ₯을 λ†’μ΄λŠ” ν˜Όν•©ν˜• 검사이닀. μ΄λŠ” 등급이 λΆ€μ—¬λ˜κ±°λ‚˜ μ„ λ°œμ΄ ν•„μš”ν•œ 성적관리 μ²΄κ³„μ—μ„œ λ™μ μžκ°€ μƒκΈ°λŠ” 것을 λ°©μ§€ν•˜κ³ , λ¬Έν•­μ˜ μˆ˜μ€€κ³Ό 풀이 μ‹œκ°„ 등을 κ³ λ €ν•˜μ—¬ κ²€μ‚¬μ˜ 타당성을 높이기 μœ„ν•¨μ΄λ‹€. κ·ΈλŸ¬λ‚˜ κ΅μœ‘μΈ‘μ •ν•™μ  μΈ‘λ©΄μ—μ„œ, κ²€μ‚¬μ˜ ꡬ성 μš”μ†Œμ— 따라 κ·Έ κ²€μ‚¬μ˜ 신뒰도가 λ‹¬λΌμ§ˆ 수 μžˆμŒμ„ μΆ©λΆ„νžˆ κ³ λ €ν•˜μ§€ μ•ŠλŠ”λ‹€λ©΄ λ‹€μ–‘ν•œ μœ ν˜•μ˜ λ¬Έν•­κ³Ό 배점방식은 였히렀 신뒰도λ₯Ό λ–¨μ–΄λœ¨λ € κ²€μ‚¬μ˜ νƒ€λ‹Ήμ„±λ§ˆμ € μœ„ν˜‘ν•  수 μžˆλ‹€. κ²€μ‚¬μ˜ 신뒰도 및 타당도와 κ΄€λ ¨ν•˜μ—¬ 차등배점에 λŒ€ν•œ λ…Όμ˜λŠ” μ„ ν–‰μ—°κ΅¬μ—μ„œ μ§€μ†μ μœΌλ‘œ μ–ΈκΈ‰λ˜μ—ˆλ‹€. 학ꡐ ν˜„μž₯μ—μ„œ 차등배점은 변별을 λͺ©μ μœΌλ‘œ μ‚¬μš©λ˜λŠ” κ²½μš°κ°€ λ§Žμ€λ°, 문항에 λ§€κ²¨μ§€λŠ” μ΄λŸ¬ν•œ κ°€μ€‘μΉ˜λŠ” 주둜 ꡐ과 μ „λ¬Έκ°€μ˜ μ„ ν—˜μ  지식과 κ΄€λ‘€ λ˜λŠ” μ „λ¬Έκ°€μ˜ νŒλ‹¨μ— μ˜ν•œ λ¬Έν•­μ˜ λ‚œμ΄λ„μ™€ μ€‘μš”λ„μ— 따라 κ²°μ •λœλ‹€. μ΄λŸ¬ν•œ μ „λ¬Έκ°€ νŒλ‹¨ 방법은 μ „λ¬Έκ°€κ°€ λ¬Έν•­κ³Ό ν”Όν—˜μžμ˜ νŠΉμ„±μ„ μΆ©μ‹€νžˆ νŒŒμ•…ν•˜μ—¬ νŒλ‹¨ν•  경우, κ²€μ‚¬μ˜ ν™œμš©λͺ©μ μ— μ ν•©ν•œ 검사 κ²°κ³Όλ₯Ό μ œκ³΅ν•˜κ³  ν”Όν—˜μžμ˜ λŠ₯λ ₯을 보닀 잘 변별할 수 μžˆλ‹€λŠ” μž₯점이 μžˆλ‹€. κ·ΈλŸ¬λ‚˜ νŒλ‹¨μ˜ μ€€κ±°κ°€ λͺ…ν™•ν•˜μ§€ μ•Šμ„ 경우 주관적이고 μž„μ˜μ μ΄λΌλŠ” λΉ„νŒμ„ 받을 수 있으며, μ΄λŸ¬ν•œ 이유둜 μΈ‘μ •ν•™μ μœΌλ‘œ νƒ€λ‹Ήν•œμ§€μ— λŒ€ν•˜μ—¬ μ„ ν–‰μ—°κ΅¬μ—μ„œ λ…Όλž€μ΄ μ œκΈ°λ˜μ–΄ μ™”λ‹€. 이 μ—°κ΅¬μ—μ„œλŠ” 동등배점과 μ°¨λ“±λ°°μ μ˜ μ—¬λŸ¬ 쑰건을 ν¬ν•¨ν•œ 배점 쑰건을 λΉ„κ΅ν•˜μ—¬ 각 쑰건에 따라 κ²€μ‚¬μ˜ 신뒰도가 μ–΄λ–»κ²Œ λ‹¬λΌμ§€λŠ”μ§€ μ•Œμ•„λ³΄κ³ , ν”Όν—˜μž λΆ„ν¬λ‚˜ λ¬Έν•­ 수의 변화에 λ”°λΌμ„œλ„ 신뒰도λ₯Ό λΉ„κ΅ν•˜μ—¬ λΆ„μ„ν•˜κ³ μž ν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•΄ μΌλ°˜ν™”κ°€λŠ₯도 이둠을 μ€‘μ‹¬μœΌλ‘œ λͺ¨μ˜μžλ£Œλ₯Ό μƒμ„±ν•˜μ—¬ κ²€μ‚¬μ˜ 신뒰도λ₯Ό λΆ„μ„ν•˜μ˜€λ‹€. λͺ¨μ˜μžλ£ŒλŠ” 졜근 8λ…„κ°„μ˜ λŒ€ν•™μˆ˜ν•™λŠ₯λ ₯μ‹œν—˜ μˆ˜λ¦¬μ˜μ—­μ˜ λ¬Έν•­ ꡬ성을 μ°Έκ³ ν•˜μ˜€μœΌλ©°, 생성쑰건은 총 4κ°€μ§€λ‘œ κ΅¬μ„±ν•˜μ—¬ λΆ„μ„ν•˜μ˜€λ‹€. μš°μ„ , 동등배점 쑰건과 차등배점 쑰건 3가지λ₯Ό κ³ λ €ν•˜μ—¬ 배점 쑰건을 κ΅¬μ„±ν•˜μ˜€κ³ , κ·Έ λ‹€μŒμœΌλ‘œ κ°œλ³„ ν•™κ΅μ—μ„œμ˜ κ²€μ‚¬μž„μ„ κ°μ•ˆν•˜μ—¬ ν”Όν—˜μž 규λͺ¨λ₯Ό 100λͺ…, 300λͺ…μœΌλ‘œ μ„€μ •ν•˜μ—¬ λͺ¨μ˜μžλ£Œλ₯Ό μƒμ„±ν•˜μ˜€λ‹€. ν”Όν—˜μž 뢄포가 μ •κ·œλΆ„ν¬λ₯Ό λ”°λ₯Ό λ•Œμ™€ λΆ€μ νŽΈν¬λ₯Ό λ”°λ₯Ό λ•Œλ‘œ λ‚˜λˆ„μ–΄ ν”Όν—˜μž 뢄포λ₯Ό μƒμ„±ν•˜μ˜€κ³ , 이후 이λ₯Ό μΌλ°˜ν™”κ°€λŠ₯도 이둠을 μ μš©ν•˜μ—¬ 500λ²ˆμ”© λ°˜λ³΅ν•˜μ—¬ λΆ„μ„ν•œ ν›„, κ·Έ 뢄석 κ³Όμ •μ—μ„œ λ¬Έν•­ μˆ˜κ°€ 30λ¬Έν•­, 25λ¬Έν•­, 20λ¬Έν•­μœΌλ‘œ 변함에 따라 κ²€μ‚¬μ˜ μ‹ λ’°λ„λŠ” μ–΄λ–»κ²Œ λ³€ν•˜λŠ”μ§€ λ˜ν•œ μ•Œμ•„λ³΄μ•˜λ‹€. 연ꡬ κ²°κ³Όλ₯Ό μš”μ•½ν•˜λ©΄ λ‹€μŒκ³Ό κ°™λ‹€. 첫째, 동등배점 μ‘°κ±΄μ—μ„œ κ²€μ‚¬μ˜ μ‹ λ’°λ„λŠ” 차등배점 μ‘°κ±΄μ—μ„œ κ²€μ‚¬μ˜ 신뒰도보닀 일반적으둜 λ†’κ²Œ λ‚˜νƒ€λ‚¬λ‹€. μ΄λŠ” 차등배점을 μ μš©ν•  νŠΉλ³„ν•œ μ΄μœ μ™€ κ·Όκ±°κ°€ μ—†λŠ” ν•œ κ²€μ‚¬μ˜ 신뒰도 μΈ‘λ©΄μ—μ„œλŠ” 동등배점을 μ μš©ν•˜λŠ” 것이 νƒ€λ‹Ήν•˜λ‹€λŠ” μ„ ν–‰μ—°κ΅¬μ˜ 결과와 μΌμΉ˜ν•œλ‹€. λ‘˜μ§Έ, 차등배점 쑰건 λ‚΄μ—μ„œ 배점 κ°„ 점수 차이가 컀질수둝 κ²€μ‚¬μ˜ μ‹ λ’°λ„λŠ” κ°μ†Œν•˜μ˜€λ‹€. λ”°λΌμ„œ 차등배점을 μ μš©ν•˜μ—¬ 검사 문항을 κ΅¬μ„±ν•˜λ”λΌλ„ 배점 차이가 크지 μ•Šλ„λ‘ μ‘°μ •ν•˜λŠ” 것이 κ²€μ‚¬μ˜ 신뒰도λ₯Ό 높이기 μœ„ν•œ μΈ‘λ©΄μ—μ„œ μ€‘μš”ν•˜λ‹€κ³  ν•  수 μžˆλ‹€. μ…‹μ§Έ, λ¬Έν•­μ˜ μˆ˜κ°€ μ€„μ–΄λ“€μˆ˜λ‘ μ‹ λ’°λ„λŠ” 비ꡐ적 크게 κ°μ†Œν•˜μ˜€λ‹€. ν”Όν—˜μž 뢄포가 μ •κ·œλΆ„ν¬λ₯Ό μ΄λ£¨λŠ” 경우 λ¬Έν•­μ˜ μˆ˜μ— μ˜ν•΄ 신뒰도가 κ°μ†Œν•˜λ”λΌλ„ μ μ •ν•œ μˆ˜μ€€μ˜ 신뒰도 .80을 λ§Œμ‘±ν•˜μ˜€μ§€λ§Œ, ν”Όν—˜μž 뢄포가 λΆ€μ νŽΈν¬μΌ κ²½μš°μ—λŠ” λ¬Έν•­ μˆ˜κ°€ 20문항일 λ•Œ, 신뒰도가 .80μ΄ν•˜λ‘œ κ°μ†Œν•˜μ˜€λ‹€. μ΄λŠ” λΆ€μ νŽΈν¬κ°€ μžˆλŠ” ν”Όν—˜μž μ§‘λ‹¨μ˜ 경우 κ²€μ‚¬μ˜ 신뒰도가 적정 μˆ˜μ€€ 이상을 μœ μ§€ν•˜κΈ° μœ„ν•˜μ—¬ 적어도 25λ¬Έν•­ 이상을 λ§Œμ‘±ν•˜λŠ” 것이 ν•„μš”ν•˜λ‹€λŠ” 것을 보여쀀닀. 이 μ—°κ΅¬λŠ” 차등배점을 κ³ λ €ν•œ ν˜Όν•©ν˜• κ²€μ‚¬μ—μ„œ 차등배점 쑰건과 ν”Όν—˜μž 뢄포, 검사 λ¬Έν•­ μˆ˜κ°€ κ²€μ‚¬μ˜ 신뒰도에 μ–΄λ–€ 영ν–₯을 λ―ΈμΉ˜λŠ”μ§€λ₯Ό μΌλ°˜ν™”κ°€λŠ₯도 이둠을 μ μš©ν•˜μ—¬ λΆ„μ„ν•˜μ˜€λ‹€λŠ” μ μ—μ„œ μ˜μ˜κ°€ μžˆλ‹€. μΆ”κ°€μ μœΌλ‘œ, 이 μ—°κ΅¬μ—μ„œ μ‚¬μš©ν•œ μΌλ°˜ν™”κ°€λŠ₯도 μ„€κ³„λŠ” ꡐ과별 κ²€μ‚¬μ˜ μ•ˆμ •μ μΈ 신뒰도λ₯Ό ν™•λ³΄ν•˜κΈ° μœ„ν•˜μ—¬ 효율적인 λ¬Έν•­ 배점 방식, λ¬Έν•­ 수 등을 μ˜ˆμ‹œν•˜λŠ”λ° ν™œμš©ν•  수 μžˆλ‹€. λ˜ν•œ 쒅합적인 평가 점수의 신뒰도λ₯Ό 높이기 μœ„ν•œ 각 ν•˜μœ„ κ²€μ‚¬μ˜ κ°€μ€‘μΉ˜ λΆ€μ—¬ 방식 λ“±μ˜ 뢄석에도 μ μš©ν•  수 μžˆμ„ κ²ƒμœΌλ‘œ κΈ°λŒ€λœλ‹€. 이 μ—°κ΅¬μ˜ μ œν•œμ μ€ μ‹€μ œ μžλ£Œκ°€ μ•„λ‹Œ λͺ¨μ˜μ‹€ν—˜ 자료λ₯Ό μƒμ„±ν•˜μ˜€κΈ° λ•Œλ¬Έμ— μ‹€μ œ μžλ£Œμ—μ„œλŠ” λ‹€λ₯΄κ²Œ λ‚˜νƒ€λ‚  수 μžˆλŠ” ν”Όν—˜μž 뢄포 λ“±μ˜ 였차 μš”μΈμ„ κ³ λ €ν•˜μ§€ λͺ»ν•˜μ˜€λ‹€λŠ” 것이닀. 또 배점 방식을 μ„ ν˜•μ μœΌλ‘œλ§Œ λ³€ν™”μ‹œμΌ°κΈ° λ•Œλ¬Έμ— λ‹€μ–‘ν•œ 배점 방식에 λ”°λ₯Έ 신뒰도λ₯Ό λΆ„μ„ν•˜μ§€ λͺ»ν–ˆλ‹€λŠ” μ μ—μ„œ ν•œκ³„λ₯Ό 가진닀. λ”°λΌμ„œ 후속 μ—°κ΅¬λ‘œ μ‹€μ œ 자료λ₯Ό λ°”νƒ•μœΌλ‘œ 쑰금 더 λ‹€μ–‘ν•œ 였차 μš”μΈκ³Ό 차등배점 λΆ€μ—¬λ°©μ‹μ˜ 닀양성을 κ³ λ €ν•˜μ—¬ 뢄석을 μ§„ν–‰ν•œλ‹€λ©΄ 보닀 의미 μžˆλŠ” 연ꡬ가 될 κ²ƒμœΌλ‘œ μƒκ°λœλ‹€.Mixed format tests with various item weights are mainly used at middle and high schools in Korea to improve discrimination of students ability. This is to keep students from having equal scores when grading is necessary, and also to increase validity by considering item difficulty and given time to solve problems. In terms of educational measurement, however, tests with various item weights can intimidate tests reliability, thereby threatening test validity, especially because test reliability depends on the test components. Research about item weights related to test reliability and validity has been conducted continuously. Item weights used in school fields are usually decided by the subject expert according to item difficulty and importance. Expert-generated item weights can be useful if experts judge the characteristics of examinee and items correctly, because they help discriminate the examinee and provide proper test results. However, when the criterion of judgement is not clear, the test can be criticized for subjectiveness and randomness. For these reasons, the appropriateness of item weights are controversial in many studies. This study analyzed and compared test reliability according to several conditions including various item weights, examinee distribution, and the number of test items. For this study, simulation data is generated and analyzed using generalizability theory. Simulation data follows the form of College Scholastic Ability Test(CSAT) in Korea and has three generating conditions such as item weights, size of examinee, and distribution of examinee. After 500 times iteration, the average reliability could be calculated, and in the process, the reliability depending on the number of items could be also calculated. The result of this study is as follows. First, test reliability using differential item weights is generally lower than the reliability of tests with equally weighted items. Similar to preceding research, this shows that differential item weights are no better than equal item weights unless there is definite reason to use various item weights. Second, the test reliability decreased as the range of item weights increased. This shows that it is important to use proper range of item weights for better reliability. Third, the test reliability decreased relatively as the number of items reduced. Especially, when the distribution of examinee was negatively skewed and the number of items was 20, the reliability was below than .80, which shows that when the examinee are distributed with skewness, the test items should consist of more than 25 items to keep appropriate reliability. This study analyzed test reliability with various item weights, two examinee distributions, and different number of items using generalizability theory. In this regard, this study illustrates efficient item weights and the number of items for stable reliability. In addition, this study can be applied to analyze the reliability of assessment consisting of various sub-tests. The limitation of this study is that because it analyzed simulation data, error factors such as different examinee distribution were beyond consideration. In this study, also, differential item weights are adjusted using only linear variation although the weight variation is not linear in school fields. For a follow-up study, therefore, it is suggested to analyze test reliability with differential item weights using real data as well as with consideration of additional error factors and variation of item weights.β… . μ„œλ‘  1 1. μ—°κ΅¬μ˜ ν•„μš”μ„± 및 λͺ©μ  1 2. 연ꡬ 문제 4 β…‘. 이둠적 λ°°κ²½ 5 1. 차등배점에 λŒ€ν•œ λ…Όμ˜ 5 2. κ³ μ „κ²€μ‚¬μ΄λ‘ μ—μ„œμ˜ 신뒰도 8 3. μΌλ°˜ν™”κ°€λŠ₯도 이둠 11 1) μΌλ°˜ν™”κ°€λŠ₯도 μ΄λ‘ μ—μ„œμ˜ 신뒰도 11 2) μΌλ°˜ν™”κ°€λŠ₯도 이둠의 단일ꡭ면섀계 13 3) λ‹€λ³€λŸ‰ μΌλ°˜ν™”κ°€λŠ₯도 이둠의 섀계 21 β…’. 연ꡬ 방법 29 1. μ—°κ΅¬μžλ£Œ 29 2. μΌλ°˜ν™”κ°€λŠ₯도 섀계 34 3. λͺ¨μ˜μžλ£Œ 생성 및 뢄석 절차 38 1) ν”Όν—˜μžμ™€ λ¬Έν•­ λͺ¨μˆ˜ 생성 38 2) 배점 쑰건에 따라 ν”Όν—˜μžμ˜ λ¬Έν•­λ°˜μ‘μžλ£Œ μž‘μ„± 40 3) μΌλ°˜ν™”κ°€λŠ₯도 뢄석 41 4) 반볡 43 β…£. 연ꡬ κ²°κ³Ό 45 1. κΈ°μˆ ν†΅κ³„ 45 2. 배점 쑰건에 λ”°λ₯Έ μ‹ λ’°λ„μ˜ λ³€ν™” 49 3. ν”Όν—˜μž 뢄포가 λΆ€μ νŽΈν¬μΌ 경우 μ‹ λ’°λ„μ˜ λ³€ν™” 54 4. λ¬Έν•­ 수 쑰건에 λ”°λ₯Έ μ‹ λ’°λ„μ˜ λ³€ν™” 58 β…€. μš”μ•½ 및 λ…Όμ˜ 63 1. μš”μ•½ 63 2. λ…Όμ˜ 66 μ°Έκ³  λ¬Έν—Œ 69 Abstract 74Maste

    (A)Study on predictors of attendance at classical music concerts and opera

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :ν˜‘λ™κ³Όμ • μŒμ•…κ΅μœ‘μ „κ³΅,2005.Maste

    The Effects of After-school Programs Participation on Academic Achievement of High School Students: A Comparison between Different Academic Achievement Level

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    이 μ—°κ΅¬μ˜ λͺ©μ μ€ μ„±μ·¨μˆ˜μ€€μ— 따라 λ‹€λ₯΄κ²Œ λ‚˜νƒ€λ‚˜λŠ” 방과후학ꡐ μ°Έμ—¬μ˜ 학업성취도 효과λ₯Ό ν™•μΈν•˜λŠ” 데 μžˆλ‹€. 이λ₯Ό μœ„ν•΄ κ΅­κ°€μˆ˜μ€€ 학업성취도 평가 2014λ…„ 쀑학ꡐ 3ν•™λ…„ μžλ£Œμ™€ 2016λ…„ μΌλ°˜κ³„ 고등학ꡐ 2ν•™λ…„ μ—°κ³„μžλ£Œλ₯Ό ν™œμš©ν•˜μ˜€λ‹€. μˆ˜ν•™κ³Ό μ˜μ–΄ κ³Όλͺ©μ—μ„œ 쀑학ꡐ μ‹œκΈ°μ˜ 개인 및 학ꡐ μˆ˜μ€€μ˜ νŠΉμ„±μ΄ 고등학ꡐ μ‹œκΈ°μ˜ 방과후학ꡐ 참여에 λ―ΈμΉ˜λŠ” 영ν–₯을 ν†΅μ œν•˜κ³ μž ꡰ집 λ‚΄ κ²½ν–₯점수 맀칭 방법을 ν™œμš©ν•˜μ˜€μœΌλ©°, 맀칭된 자료λ₯Ό λ°”νƒ•μœΌλ‘œ 닀측뢄석을 μ‹€μ‹œν•˜μ˜€λ‹€. μ΄λ•Œ μ„±μ·¨ μˆ˜μ€€μ— 따라 집단을 보톡이상 ν•™λ ₯κ³Ό κΈ°μ΄ˆμ΄ν•˜ ν•™λ ₯으둜 κ΅¬λΆ„ν•˜μ—¬ λΆ„μ„ν•˜κ³ , μΆ”κ°€μ μœΌλ‘œ λ°©κ³Όν›„ν•™κ΅μ˜ νš¨κ³Όκ°€ μ§€μ—­κ·œ λͺ¨μ˜ 영ν–₯을 λ°›λŠ”μ§€ μ‚΄νŽ΄λ³΄μ•˜λ‹€. λΆ„μ„κ²°κ³ΌλŠ” λ‹€μŒκ³Ό κ°™λ‹€. 첫째, ν•™λ ₯μˆ˜μ€€κ³Ό 쀑학ꡐ μ‹œκΈ°μ˜ 사전 νŠΉμ„±μ— 따라 λ°©κ³Όν›„ 학ꡐ μ°Έμ—¬ λΉ„μœ¨μ€ λ‹€λ₯Έ 양상을 보여, λ°©κ³Όν›„ν•™κ΅μ˜ 효과λ₯Ό λΆ„μ„ν•˜κΈ° μœ„ν•˜μ—¬ 방과후학ꡐ ν”„λ‘œκ·Έλž¨μ˜ μ°Έμ—¬λ₯Ό 선택할 μ„ νƒνŽΈμ˜λ₯Ό κ³ λ €ν•΄μ•Ό ν•  ν•„μš”κ°€ μžˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€. λ‘˜μ§Έ, μˆ˜ν•™κ³Ό μ˜μ–΄ κ³Όλͺ©μ—μ„œ μ„±μ·¨μˆ˜μ€€κ³Ό 상관없이 방과후학ꡐ에 μ°Έμ—¬ν•˜λŠ” 경우 ν•™μƒλ“€μ˜ 학업성취도가 더 λ†’κ²Œ λ‚˜νƒ€λ‚¬μœΌλ©°, κ·Έ νš¨κ³ΌλŠ” κΈ°μ΄ˆμ΄ν•˜ ν•™λ ₯일 λ•Œ 더 높은 κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚˜ 방과후학ꡐ 정책이 κΈ°μ΄ˆν•™λ ₯ μ΄ν•˜ ν•™μƒλ“€μ—κ²Œ μƒλŒ€μ μœΌλ‘œ 더 도움이 λ˜μ—ˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€. μ…‹μ§Έ, 방과후학ꡐ μ°Έμ—¬ 여뢀와 학업성취도 μ μˆ˜λŠ” μ§€μ—­κ·œλͺ¨μ— λ”°λΌμ„œ κ·Έ 차이가 크게 λ‘λ“œλŸ¬μ§€κ²Œ λ‚˜νƒ€λ‚˜μ§€ μ•Šμ•˜λ‹€. 이 μ—°κ΅¬λŠ” 방과후학ꡐ μ°Έμ—¬ 여뢀에 영ν–₯을 λ―ΈμΉ˜λŠ” μ„ νƒνŽΈμ˜λ₯Ό ν†΅μ œν•˜κ³  학ꡐ μˆ˜μ€€μ„ κ³ λ €ν•˜μ—¬ λΆ„μ„ν•¨μœΌλ‘œμ¨ μ„±μ·¨μˆ˜μ€€μ— 따라 λ‹€λ₯΄κ²Œ λ‚˜νƒ€λ‚ μˆ˜ μžˆλŠ” 방과후학ꡐ 효과λ₯Ό 보닀 μ—„λ°€ν•˜κ²Œ λΆ„μ„ν•˜μ˜€λ‹€λŠ” μ μ—μ„œ μ˜μ˜κ°€ μžˆλ‹€. μ΄λŸ¬ν•œ 연ꡬ κ²°κ³Όλ₯Ό λ°”νƒ•μœΌλ‘œ 곡ꡐ윑 λ‚΄μ‹€ν™”λΌλŠ” 방과후학ꡐ 운영 λͺ©μ μ— λΉ„μΆ”μ–΄ μ—°κ΅¬μ˜ μ‹œμ‚¬μ μ„ λ„μΆœν•˜μ˜€λ‹€

    Exploring Roles of Feedback to Facilitate Online Discu

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    온라인 토둠에 κ΄€ν•œ μ‹œκ°μ  ν”Όλ“œλ°±μ„ μ œκ³΅ν•¨μœΌλ‘œμ¨ ν•™μŠ΅μž κ°„μ˜ μƒν˜Έμž‘μš©μ„ ν–₯μƒμ‹œν‚€λ €λŠ” λ…Έλ ₯이 μ¦κ°€ν•˜κ³  μžˆλ‹€. 온라인 ν† λ‘  ν”Όλ“œλ°±μ˜ 효과λ₯Ό μ¦μ§„μ‹œν‚€κΈ° μœ„ν•΄μ„œλŠ” ν•™μŠ΅μžκ°€ ν”Όλ“œλ°±μ„ μ–΄λ–»κ²Œ μΈμ‹ν•˜κ³  온라인 ν† λ‘  ν–₯상을 μœ„ν•΄ μ–΄λ–»κ²Œ ν™œμš©ν•˜λŠ”μ§€μ— λŒ€ν•œ 체계적인 연ꡬ가 ν•„μš”ν•˜λ‹€. 이 μ—°κ΅¬μ—μ„œλŠ” 온라인 ν† λ‘  ν”Όλ“œλ°±μ˜ 역할을 온라인 ν† λ‘ μ˜ ν–₯상과 ν•™μŠ΅μžμ˜ ν”Όλ“œλ°±μ— λŒ€ν•œ μ„±μ°° 및 인식을 μ€‘μ‹¬μœΌλ‘œ μ‘°μ‚¬ν•˜μ˜€λ‹€. μ„œμšΈ μ†Œμž¬ μ’…ν•©λŒ€ν•™κ΅μ— μž¬ν•™μ€‘μΈ 109λͺ…μ˜ ν•™μŠ΅μžκ°€ 1차와 2μ°¨ 온라인 토둠에 μ°Έμ—¬ν•˜μ˜€λ‹€. 각 ν•™μŠ΅μžλŠ” 1μ°¨ ν† λ‘  이후에 온라인 ν† λ‘  참여도, μ°Έμ—¬μ‹œκΈ°, μƒν˜Έμž‘μš© νŒ¨ν„΄, ν† λ‘  κΈ€ μœ ν˜•μ— λŒ€ν•œ ν”Όλ“œλ°±μ„ μ˜¨λΌμΈμ—μ„œ μ œκ³΅λ°›κ³  μžμ‹ μ˜ ν† λ‘ ν™œλ™μ— λŒ€ν•œ 성찰을 ν•˜μ˜€λ‹€. 1차와 2μ°¨ 토둠을 λΉ„κ΅ν•˜μ˜€μ„ λ•Œ, 온라인 ν† λ‘  ν”Όλ“œλ°±μ€ 온라인 ν† λ‘ μ˜ 참여도, 졜초 μ°Έμ—¬μ‹œκΈ°, ν•™μŠ΅μž κ°„ μƒν˜Έμž‘μš©, ν† λ‘  κΈ€μ˜ μœ ν˜•μ— μœ μ˜λ―Έν•œ 영ν–₯을 λ―Έμ³€λ‹€. 그리고 온라인 ν† λ‘ μ˜ κ°œμ„ μ μ„ ꡬ체적이고 μžμ„Ένžˆ μ„±μ°°ν• μˆ˜λ‘ 2μ°¨ 온라인 토둠에 더 적극적으둜 μ°Έμ—¬ν•˜κ³  λ‹€λ₯Έ ν•™μŠ΅μžμ™€ ν™œλ°œν•˜κ²Œ μƒν˜Έμž‘μš©μ„ ν•˜μ˜€λ‹€. λŒ€λ‹€μˆ˜μ˜ ν•™μŠ΅μžκ°€ 온라인 ν† λ‘  ν”Όλ“œλ°±μ΄ μœ μš©ν•˜κ³  μš©μ΄ν•˜λ‹€κ³  μΈμ‹ν•˜μ˜€μœΌλ©°, 긍정적인 νƒœλ„λ₯Ό λ³΄μ˜€λ‹€. 특히, ν† λ‘  μ°Έμ—¬μ‹œκΈ°μ™€ μƒν˜Έμž‘μš© νŒ¨ν„΄ ν”Όλ“œλ°±μ— λŒ€ν•œ νƒœλ„μ™€ μœ μš©μ„± 및 μš©μ΄μ„±μ— λŒ€ν•œ 인식이 λ†’κ²Œ λ‚˜νƒ€λ‚¬λ‹€. μ΄λŸ¬ν•œ 연ꡬ결과에 κΈ°λ°˜ν•΄μ„œ ν–₯ν›„ 온라인 ν† λ‘  ν”Όλ“œλ°± 연ꡬ에 κ΄€ν•œ μ œμ–Έμ„ ν•˜μ˜€λ‹€.A growing number of studies have made efforts to provide students with feedback by visualizing online discussion activities. In order to enhance the effectiveness of online discussion feedback, more research needs to investigate how learners perceive online discussion feedback and how they use it for improving their online discussion. This study was carried out to investigate the roles of four types of online discussion feedback and learner perceptions toward the feedback in higher education. For this study, 109 undergraduates enrolled at a university in Seoul participated in the first and second online discussion. After the first online discussion, they reflected on each of the feedback about their online discussion frequency, participation time, interaction patterns, and discussion message types. This study found that online discussion feedback significantly influenced online discussion frequency, first participation time, student-to-student interaction, and discussion message types. In addition, learners who specifically and deeply reflected on their feedback more successfully carried out the second online discussion. Learners also had positive attitudes toward the four types of feedback and positively perceived their usefulness and easy of use. Particularly, the attitude, perceived usefulness, and perceived easy of use were higher for the feedback of participation time and interaction patterns than the other feedback. Based on these findings, we discussed how to improve online discussion feedback in the future research
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