The Future of Social Learning: A Novel Approach to Connectivism

The primary goal of this paper is to operationalize the connectivism approach into a new learning model with additions from problem-based and contextual learning that can be effectively implemented together, to improve socioeconomically diverse learners’ educational outcomes (attitude and persistence) in STEM (Science, Technology, Education and Mathematics) areas. We model this approach through the development and demonstration of an innovative, evidence-based, and scalable how-to-learn program that leverages connectivism principles and technology. This paper uses the case of energy education to provide contextual relevancy and prepare learners for the high demand jobs of the future. The new model is developed within the context of Internet of Things (IoT), where students have a unique opportunity to participate in a real-world application of an IoT system for green energy governance

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and Social Network Analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ( big data ) in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social Network Analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and Social Network Analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms

Quantification of students’ active learning in design, build, and test engineering modules

The focus in this paper is to address the primary research question ‘How can instructors leverage assessment tools in design, build, and test modules to quantify students’ active learning well enough to improve modules for future students?’ In the engineering module, Product Design Group Project (PDGP), the primary goal is to enable students to internalize five principles of engineering design (POED), wherein each assignment students are tasked with writing learning statements (LS). LS captures how much students internalize the target POED and formulate an understanding of how to apply this knowledge moving forward. Each academic year in the PDGP module, at a university in north-west England, around 780 LS are consented by module students. In this paper, a flexible text mining framework is used to process LS and analyse students’ learning and improve the delivery of design, build, and test engineering modules, such as the PDGP

Teacher-Student Relationships Influencing Classroom Management of Challenging Behaviors: A Case Study

The purpose of this case study was to understand how teacher-student relationships influence classroom management of students with challenging behaviors for teachers of ninth- grade Multi-tiered System of Supports (MTSS) students at Blue High School. The theory guiding this study was Glasser’s choice theory. All human behavior is driven by the desire to satisfy the five basic needs: survival, freedom, fun, power, and love. Behaviors happen because of personal choices made in an attempt to fulfill those needs. This idea served as a framework for all research and data collection. The method used for this case study was qualitative with a multi-case study design. A distinct analytical approach was used to understand how teacher-student relationships influence classroom management of students who display challenging behaviors. This study consisted of ten10 to twelve 12 teachers who teach ninth-grade students in the MTSS program at Blue High School. Observations of teachers were recorded, and interviews followed the completion of those observations. Further, document analysis was used regarding students’ discipline records provided by the principal of Blue High School. Yin’s research design guided all research procedures. Data analysis was completed along with, pattern matching, explanation building, and cross-case synthesis that analyzed all collected information on the themes of communication, inconsistent teacher response, inclusion and belonging, teacher- student relationships, and correcting challenging behaviors

Analyzing Social Construction of Knowledge and Social Networks in Online Discussion Forums in Spanish

This mixed methods research project examined social construction of knowledge and social networks in three non-structured student centered online discussion forums, which were part of a graduate online course on web conferencing in Spanish within the Mexican sociocultural context. The purpose of the study was to identify interaction patterns among twenty-one graduate students by analyzing discussion forum posts, measuring student centrality, and generating social network diagrams in order to explain the characteristics of posts and social networks that may contribute to social construction of knowledge. The researcher used a sequential approach, starting with the application of an interaction analysis model and social network analysis, followed by a combination of both analyses to shed light on interaction in online discussion forums carried out in Spanish. The researcher found evidence of interaction patterns that suggest a possible relationship between the centrality measure in-degree and high levels of social construction of knowledge, furthermore results suggest dissonance or disagreement in student-to-student interaction may also contribute to the achievement of more complex phases of social construction of knowledge

Pengembangan Model Pengukuran Meaningful Learning Berdasarkan Semantik Aktivitas Pelajar Dalam Lingkungan E-Learning

Sistem e-learning telah digunakan secara luas di dunia, termasuk di Indonesia. Perkembangan e-learning yang pesat menyebabkan evaluasi efektivitas e-learning semakin rumit. Hal ini karena e-learning melibatkan banyak komponen, salah satunya adalah pelajar. Pengukuran efektifitas e-learning dapat dilakukan melalui observasi keterlibatan pelajar dalam e-learning. Di lain pihak, tujuan utama pendidikan di setiap level adalah harus melibatkan pelajar di dalam meaningful learning. Keterlibatan pelajar di dalam meaning learning terjadi ketika pelajar dapat menciptakan makna. Dengan demikian, pengembangan sebuah model untuk mengukur meaningful learning pelajar berdasarkan semantik dalam lingkungan e-learning menjadi penting untuk dilakukan. Tujuan dari penelitian ini adalah untuk menghasilkan model yang mampu mengukur keterlibatan pelajar untuk kelima karakteristik meaningful learning berdasarkan semantik dalam lingkungan e-learning. Pembuatan model dilakukan dengan membangun metrik koefisien pemetaan antara karakteristik meaningful learning dan aktivitas Moodle dan mengembangkan model perhitungan nilai karakteristik meaningful learning pelajar. Pembuatan metrik koefisien pemetaan dilakukan melalui perhitungan keserupaan semantik antara fakta kunci karakteristik meaningful learning dan fakta kunci tindakan aktivitas Moodle. Hasil pemetaan dan pengolahan data pelajar diintegrasikan ke dalam sebuah model perhitungan nilai karakteristik meaningful learning pelajar. Pengolahan data pelajar melibatkan data konten dan data konteks. Data konten yang dilibatkan adalah frekuensi tindakan pelajar. Data konteks yang dilibatkan adalah data relevansi tindakan aktivitas blog, chat, forum, glossary, dan wiki, serta tingkat kognitif pesan dalam forum. Penentuan relevansi dilakukan dengan menggunakan keserupaan semantik aktivitas pelajar. Kerangka kerja penentuan relevansi pesan terdiri dari proses pengumpulan dataset, pendeteksian relevansi pesan, dan proses pengujian. Sedangkan kerangka kerja penentuan tingkat kognitif pesan terdiri dari proses pembuatan corpus, proses ekstraksi dan pemilihan fitur, proses pengembangan penglasifikasi dan pelatihan, dan proses pengujian. Penentuan relevansi didasarkan pada nilai ambang batas (threshold) dari keserupaan semantik. Threshold optimal yang diperoleh untuk pesan forum ke forum 0.6, pesan ke parent 0.59. Threshold optimal untuk chat, blog, glossary, dan wiki ke mata kuliah masing-masing 0.581, 0.86, 0.66, dan 0.82. Sedangkan hasil penentuan tingkat kognitif pesan yang optimal ditentukan oleh jumlah kata kunci kata benda (noun) dan kata sifat (adjective), serta jumlah tanda baca (punctuation). Perbandingan antara hasil pengukuran tingkat keterlibatan meaningful learning pelajar dari model yang dikembangkan dalam penelitian ini dan hasil pengukuran para anotator tidak berbeda secara signifikan. Hal ini ditunjukkan oleh rerata hasil uji Wilcoxon yang berada diantara –Z0.5/2=-1.96 dan Z0.5/2=1.96, yaitu sebesar -0.478. Selain itu, hasil uji intraclass correlation coefficient (ICC) menunjukkan bahwa model pengukuran yang dikembangkan memiliki tingkat reliabilitas yang tinggi dengan rataan nilai ICC sebesar 0.863 ====================================================================================================================================== The e-learning system has been widely used around the world, including in Indonesia. The rapid development of e-learning has made the evaluation of elearning effectiveness become increasingly complicated due to the involvement of the learner. Since the main goal of education at every level is to ensure the learner involvement in meaningful learning and such an involvement can occur when the learner is enabled to create meaning; therefore, the development of a model for measuring meaningful learning based on semantic activities of the learner in elearning environment become important. The aim of this research is to produce a model that is capable of measuring learner involvement for the five characteristics of meaningful learning based on semantic activities of the learner in e-learning environment. The model was developed by constructing the mapping coefficient metric between meaningful learning characteristics as well as Moodle activities and developing a model that is capable of measuring the value of learner’s meaningful learning. The mapping coefficient metric was generated by calculating the semantic similarity between the key facts of meaningful learning characteristics and the key facts of Moodle activity actions. Results of the mapping and processing of learner data containing content and context data were integrated into the model. The content data was essentially the frequencies of learner’s activity actions, while the context data included the relevance of activity data of blogs, chats, forums, glossaries, and wikis, as well as the cognitive level of messages in the forum. Determination of relevance among activity data of the learner was based on their semantic similarity. The framework for determining message relevance consists of data collection process, message relevance detection, and testing process. Whilst, the framework for determining the cognitive level of the message consists of the process of creating the corpus, extracting and selecting of features, classifier development and training, and testing process. A threshold of the similarity of semantic activities of the learner was used to determine the relevance among those activities. Experimental results show that the optimal threshold for forum-messages to forum and forum-messages to their parent are 0.6 and 0,59, respectively. The optimal threshold for chat, blog, glossary, and wiki to courses are 0.581, 0.86, 0.66, and 0.82, respectively. While the optimal result of determining the cognitive level of the message is determined by the number of noun and adjective keywords, as well as the number of punctuations. The comparison between results of measuring the meaningful learning involvement level of the learner from the model and results of the annotators was not significantly different. This is indicated by the average value of Wilcoxon test that lies between –Z0.5/2=-1.96 and Z0.5/2=1.96, i.e. -0.478. In addition, intra-class correlation coefficient (ICC) test results show that the model has a high level of reliability with an average ICC value of 0.86