
Ramazan Yılmaz
Ramazan Yılmaz is an Assoc Professor in the Department of Computer Education & Instructional Technology
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Papers by Ramazan Yılmaz
Bu çalışmanın amacı, üretken yapay zeka için Birleşik Teknoloji Kabul ve Kullanım Teorisi (UTAUT) modeline dayanan bir kabul ölçeği geliştirmektir. Ölçek, öğrencilerin üretken yapay zeka uygulamalarını kabulünü incelemek için tasarlanmıştır. Bu araç, öğrencilerin üretken yapay zeka uygulamalarına yönelik kabul düzeylerini değerlendirmektedir. Ölçek geliştirme çalışması, 2022-2023 akademik yılı boyunca ChatGPT gibi üretken üretken yapay zeka araçlarını kullanan çeşitli fakültelerden 627 üniversite öğrencisini kapsayan üç aşamada gerçekleştirilmiştir. Ölçeğin görünüş ve kapsam geçerliliğini değerlendirmek için alanda uzman profesyonellerden görüş alınmıştır. İlk örneklem grubuna (n = 338) altta yatan faktörleri keşfetmek için açımlayıcı faktör analizi (AFA) uygulanırken, sonraki örneklem grubuna (n = 250) faktör yapısının doğrulanması için doğrulayıcı faktör analizi (DFA) uygulanmıştır. Daha sonra, AFA sonucunda 20 maddeden oluşan dört faktörün toplam varyansın %78.349'unu açıkladığı görülmüştür. DFA sonuçları, ölçeğin 20 madde ve dört faktörden (performans beklentisi, çaba beklentisi, kolaylaştırıcı koşullar ve sosyal etki) oluşan yapısının elde edilen verilerle uyumlu olduğunu doğrulamıştır. Güvenilirlik analizi Cronbach alfa katsayısını 0.97, test-tekrar test yöntemi ise 0.95'lik bir güvenilirlik katsayısı ortaya koymuştur. Maddelerin ayırt edici gücünü değerlendirmek için, katılımcıların alt %27'si ile üst %27'si arasında karşılaştırmalı bir analiz yapılmış ve ardından düzeltilmiş madde-toplam korelasyonları hesaplanmıştır. Sonuçlar, üretici yapay zeka kabul ölçeğinin geçerlik ve güvenilirliğinin yüksek olduğunu ve böylece sağlam bir ölçüm aracı olarak etkinliğini teyit ettiğini göstermektedir.
Anahtar Kelimeler: Açık ve uzaktan eğitim, uzaktan öğrenme, sınıf yönetimi ve bileşenleri
Anahtar sözcükler: Modelleme, simülasyon, simülasyon, uzamsal düşünme, sezgisel ve olasılığa dayalı düşünme, temsillerle düşünme
MOOCs allow learners from all over the world to specialize in specific subjects and receive certifications, regardless of their location or educational institution. Learners can progress at their own pace without being constrained by a curriculum, use the materials indefinitely, study independently of time and location, and constantly test themselves in such environments. Moreover, learners can experience all of these for little or no charge. Learners, on the other hand, leave a massive amount of data in these environments.The field of learning analytics is concerned with intervening in the learning process based on these data. Two main information sources are frequently considered for data-driven interventions. One of these sources is previous learning experiences. In other words, it's the information from learners who have experienced similar learning conditions before. The literature is another source of data that can be used to develop instructional interventions. The design principles proposed in the literature can serve as the basis for instructional interventions. At this point, this study will provide empirical findings for the community of education who will evaluate the literature as a data source.
This research also aims to introduce the MOOC platform developed by the authors using HTML, PHP, JavaScript, and MySQL. Learners can select content types based on their preferences in the developed system. In addition to video lectures, the system offers textual presentations, e-books, infographics, and alternative videos as instructional sources. Learners can utilize descriptive analytics to see their current situation, as well as predictive analytics to evaluate their next learning experience. Learners' entire interaction data are stored, and the system presents the required information via learning analytics dashboard. To do this, the system employs both classification and clustering algorithms in this process. The system also functions as an intelligent tutoring system while the learners are taking the competency test. The system will direct the learner to this module if he fails the competency test without help. The goal of this module is to improve learners’ problem-solving skills by using scaffolding strategies like hints and worked examples. Detailed information about the system's features will be provided during the session. However, as previously stated, the study's main goal is to figure out how the learners' interaction data in this system corresponds with psycho-educational variables.
The study involved 156 students from three different state universities during the 2020-2021 academic year, 20 of whom were graduate students and 136 undergraduate students. Within the context of the Statistics course, students used the developed MOOC platform for 8 weeks. Learners’ interactions with the system were recorded and analyzed during this period. In addition, the scale developed by Pintrich, Smith, Garcia, and McKeachie (1991) was used to assess students' self-reported motivational beliefs. Within the scope of the study, the data of 90 students who responded to this scale were analyzed. The motivational beliefs dimension of the scale was used in the context of the study, but not the learning strategies dimension. In this context, intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning and test anxiety were examined.
The main goal of this study is to discover the relationship between student interactions and motivational beliefs in the online environment. Test anxiety and intrinsic goal orientation were found to have an impact on the success of competence tests as a result of the research. In other words, it has been discovered that learners' curiosity about the system and its contents, combined with their test anxiety, has an impact on their performance in the system. Furthermore, students' level of appreciation for the system's content was found to be related to their belief in control over learning, or in other words, the belief that their own efforts affect their learning outcomes. The duration of the learners' navigation in the system was found to be significantly affected by their task value, which is their perception of the system's importance and benefit, as well as the tasks it contains. According to the findings, while undergraduate and graduate students performed similarly on competency tests, graduate students' task value was significantly higher, while undergraduate students' test anxiety and extrinsic goal orientations were superior to graduate students. In addition to these variables specified in the study, the relationships of motivational beliefs with various variables were also evaluated.
Bu çalışmanın amacı, üretken yapay zeka için Birleşik Teknoloji Kabul ve Kullanım Teorisi (UTAUT) modeline dayanan bir kabul ölçeği geliştirmektir. Ölçek, öğrencilerin üretken yapay zeka uygulamalarını kabulünü incelemek için tasarlanmıştır. Bu araç, öğrencilerin üretken yapay zeka uygulamalarına yönelik kabul düzeylerini değerlendirmektedir. Ölçek geliştirme çalışması, 2022-2023 akademik yılı boyunca ChatGPT gibi üretken üretken yapay zeka araçlarını kullanan çeşitli fakültelerden 627 üniversite öğrencisini kapsayan üç aşamada gerçekleştirilmiştir. Ölçeğin görünüş ve kapsam geçerliliğini değerlendirmek için alanda uzman profesyonellerden görüş alınmıştır. İlk örneklem grubuna (n = 338) altta yatan faktörleri keşfetmek için açımlayıcı faktör analizi (AFA) uygulanırken, sonraki örneklem grubuna (n = 250) faktör yapısının doğrulanması için doğrulayıcı faktör analizi (DFA) uygulanmıştır. Daha sonra, AFA sonucunda 20 maddeden oluşan dört faktörün toplam varyansın %78.349'unu açıkladığı görülmüştür. DFA sonuçları, ölçeğin 20 madde ve dört faktörden (performans beklentisi, çaba beklentisi, kolaylaştırıcı koşullar ve sosyal etki) oluşan yapısının elde edilen verilerle uyumlu olduğunu doğrulamıştır. Güvenilirlik analizi Cronbach alfa katsayısını 0.97, test-tekrar test yöntemi ise 0.95'lik bir güvenilirlik katsayısı ortaya koymuştur. Maddelerin ayırt edici gücünü değerlendirmek için, katılımcıların alt %27'si ile üst %27'si arasında karşılaştırmalı bir analiz yapılmış ve ardından düzeltilmiş madde-toplam korelasyonları hesaplanmıştır. Sonuçlar, üretici yapay zeka kabul ölçeğinin geçerlik ve güvenilirliğinin yüksek olduğunu ve böylece sağlam bir ölçüm aracı olarak etkinliğini teyit ettiğini göstermektedir.
Anahtar Kelimeler: Açık ve uzaktan eğitim, uzaktan öğrenme, sınıf yönetimi ve bileşenleri
Anahtar sözcükler: Modelleme, simülasyon, simülasyon, uzamsal düşünme, sezgisel ve olasılığa dayalı düşünme, temsillerle düşünme
MOOCs allow learners from all over the world to specialize in specific subjects and receive certifications, regardless of their location or educational institution. Learners can progress at their own pace without being constrained by a curriculum, use the materials indefinitely, study independently of time and location, and constantly test themselves in such environments. Moreover, learners can experience all of these for little or no charge. Learners, on the other hand, leave a massive amount of data in these environments.The field of learning analytics is concerned with intervening in the learning process based on these data. Two main information sources are frequently considered for data-driven interventions. One of these sources is previous learning experiences. In other words, it's the information from learners who have experienced similar learning conditions before. The literature is another source of data that can be used to develop instructional interventions. The design principles proposed in the literature can serve as the basis for instructional interventions. At this point, this study will provide empirical findings for the community of education who will evaluate the literature as a data source.
This research also aims to introduce the MOOC platform developed by the authors using HTML, PHP, JavaScript, and MySQL. Learners can select content types based on their preferences in the developed system. In addition to video lectures, the system offers textual presentations, e-books, infographics, and alternative videos as instructional sources. Learners can utilize descriptive analytics to see their current situation, as well as predictive analytics to evaluate their next learning experience. Learners' entire interaction data are stored, and the system presents the required information via learning analytics dashboard. To do this, the system employs both classification and clustering algorithms in this process. The system also functions as an intelligent tutoring system while the learners are taking the competency test. The system will direct the learner to this module if he fails the competency test without help. The goal of this module is to improve learners’ problem-solving skills by using scaffolding strategies like hints and worked examples. Detailed information about the system's features will be provided during the session. However, as previously stated, the study's main goal is to figure out how the learners' interaction data in this system corresponds with psycho-educational variables.
The study involved 156 students from three different state universities during the 2020-2021 academic year, 20 of whom were graduate students and 136 undergraduate students. Within the context of the Statistics course, students used the developed MOOC platform for 8 weeks. Learners’ interactions with the system were recorded and analyzed during this period. In addition, the scale developed by Pintrich, Smith, Garcia, and McKeachie (1991) was used to assess students' self-reported motivational beliefs. Within the scope of the study, the data of 90 students who responded to this scale were analyzed. The motivational beliefs dimension of the scale was used in the context of the study, but not the learning strategies dimension. In this context, intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning and test anxiety were examined.
The main goal of this study is to discover the relationship between student interactions and motivational beliefs in the online environment. Test anxiety and intrinsic goal orientation were found to have an impact on the success of competence tests as a result of the research. In other words, it has been discovered that learners' curiosity about the system and its contents, combined with their test anxiety, has an impact on their performance in the system. Furthermore, students' level of appreciation for the system's content was found to be related to their belief in control over learning, or in other words, the belief that their own efforts affect their learning outcomes. The duration of the learners' navigation in the system was found to be significantly affected by their task value, which is their perception of the system's importance and benefit, as well as the tasks it contains. According to the findings, while undergraduate and graduate students performed similarly on competency tests, graduate students' task value was significantly higher, while undergraduate students' test anxiety and extrinsic goal orientations were superior to graduate students. In addition to these variables specified in the study, the relationships of motivational beliefs with various variables were also evaluated.