Analysis of Online Interaction Patterns in Online Debate of High School Students through SNA

(375) - Weighted Gene Co-expression Network Analysis Reveals Distinct Interaction Patterns in Failing and Recovering Human Myocardium

The analysis of learners’ social capital development strategies provides an important perspective for understanding the mechanisms and strategies of connectivist learning. This study employs community detection algorithms, social network analysis, and SIENA to explore the diverse strategies connectivist learners use to accumulate social capital and assess their effectiveness, based on longitudinal observation and analysis of subcommunity interaction patterns in cMOOC communities. This study identifies three subcommunities with distinct interaction and social capital characteristics: the “budding community”, “partially cohesive community”, and “integrated community”. Six accumulation strategies are defined: rational reciprocity, closed-loop reciprocity, relational bridging, influence-following, social connector engagement, and homophilic proximity. Each subcommunity exhibits unique patterns in strategy application, and the organic integration of multiple strategies plays a key role in achieving the effective and balanced development of social capital. The outstanding performance of the integrated community in social capital accumulation indicates that early proactive and widespread rational reciprocity and relational bridging, combined with sustained influence following in later stages, foster a multihub community leadership pattern. This promotes network convergence, stronger connectivity, and the simultaneous advancement of both bonding and bridging social capital. The study offers significant insights for optimizing the construction of online learning communities, course design, and learning support.

This study aims to reveal the interaction patterns and inhibiting factors for the final project guidance in the Mathematics and Natural Sciences Education Department, Universitas Lampung, to develop the six main parameters of the Indonesian National Qualifications Framework (INQF). The study employed a mixed method involving 67 students who composed the final project for the 2021/2022 academic year selected from 314 final project students using the cluster random sampling technique. The data in this study were primary and secondary data obtained by questionnaires, interviews, and documents, analyzed qualitatively, quantitatively, and descriptively. The closed-response questionnaire was used regarding the interaction pattern, the INQF parameters achievement, and perceptions of the achievement of students' core competencies. An open-response questionnaire regarding the inhibiting factors for the final project guidance was used to provide the qualitative data. Descriptive analysis was conducted to determine the profile and interaction pattern of the final project guidance, while the qualitative analysis using Miles and Huberman model was to reveal the inhibiting factors of the final project. The results show that: (1) the dominant interaction pattern that occurred was an associative pattern originating from the factors of suggestion, identification, and sympathy; (2) the INQF parameter with the best score is the science parameter with an average parameter achievement of 9.97 from the lowest score 3.00 until the highest score 12.38; and (3) the perception of achieving the highest core competency of students is the aspect of soft skills, both personal and interpersonal, with the highest average perception of achieving core competency being 59.71 out of 80.

Web pages could be understood as graphical user interfaces to domain specific data provided by the internet. The specification of HTML5 facilitates the realization of user interfaces based on open standards and thereby the easy integration of code for user-tracking in already existing web pages. This paper describes a system for the detection and analysis of patterns within the user-traces. The core component of the system is a module that provides stochastic models for capturing patterns. In contrast to the simple detection of patterns within a stream of interaction events a model based approach condenses the patterns to states. The system enables an analyst to define restrictions to the state space and facilitates the interpretation of the found states.

This paper is based on the analysis of a focus group interview of a moderator and a group of undergraduate students on the topic of self-regulation of learning. The purpose of the investigation was to identify interaction patterns that appeared in the talk of participants and the moderator. In the stream of communication two rudimentary interaction patterns were recognized. The first pattern was named the Catalogue. It consists of a sequence of turns of participants who respond to a request of the moderator and who provide their answers, one by one, without reacting on the content of the previous partner(s) talk. The other interaction pattern was called the Domino. In this pattern participants respond to each other. The Catalogue pattern prevailed in the interview. Alongside with identification of patterns of interaction the study demonstrated the functions of the common ground and its accomplishment in the talk of the moderator and participants.

<p>Discussions are commonly used in online teaching and have been shown to foster student learning and collaboration. This case study uses content analysis to explore the interaction patterns of student online discussions during a semester-long teacher preparation course using concepts from sociometry. Findings suggest that interaction patterns were influenced by the content of student posts. Online discussions in this case were found to be an equitable form of collaborative learning, enabling each student to have a voice. There were, however, indications that gendered ways of knowing may play a role in the content of interaction, if not in the patterns themselves.</p>

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

This study aims to investigate the interaction patterns between the teacher and students or among students themselves in a Korean elementary English classroom by analysing classroom talk and by focusing on the types of the teacher’s questions and feedback as well as the use of L1 and L2. This analysis is used to determine the influence of such interaction on the students’ communicative skills in English. A female teacher in Gyeonggi province and a 6th-grade class participated in the data collection. This study included data from video or audio recordings, interviews, field notes, self-assessments, and portfolios. The transcribed data were analysed using Tsui’s (1994) framework and Verplaetse’s (1995, 2000) coding system. The results of this analysis indicated that the interaction patterns varied between participants into three groups: teacher-initiated talk (e.g., T-S talk) vs students-initiated talk (e.g., S-T talk & S-S talk). At the beginning stage of lesson, the teacher-initiated talk made up the majority of talk in English, and facilitated the students’ involvement in classroom learning including their language use. In the S-S talk, the students were involved in interaction which occurred naturally without the teacher’s control.

Emergent patterns in agent-environment interactions and their roles in supporting agile spatial skills

To the best of our knowledge, the current study could be considered the first comprehensive one based on the application of molecular docking on the non-nucleoside thumb and palm inhibitors to nonstructural NS5B protein for the detailed evaluation of their binding patterns in the corresponding binding regions in the protein. Non-nucleoside thumb and palm inhibitors were docked into the thumb and palm sites of the nonstructural NS5B protein which is the RNA-dependent RNA polymerase, respectively. Two docking programs, AutoDock 4.2 and AutoDock Vina were employed for the docking of thumb inhibitors (filibuvir and lomibuvir) and palm inhibitors (dasabuvir and nesbuvir) into the respective binding region. The preliminary analysis of the docking performance demonstrated that AutoDock Vina was suitable for the docking of large flexible molecules as deduced from the alignment of docking conformations in all cases. Based on the docking calculation, the interaction pattern analysis was carried out for all the inhibitors which were found to reside in the respective binding region. Before the interaction pattern analysis, the best docking pose was selected based on the high binding affinity value. The interaction pattern analysis of the inhibitors revealed that hydrogen bonding and hydrophobic contacts with amino acid residues of the respective binding region were the leading force in the stabilization of these inhibitors besides other interactions. The interaction pattern analysis based on the docking calculation was also realized to be the meaningful approach to figuring out the relative stability of the protein-ligand complex formed after the inhibitor binding which eventually led to assessing the inhibitory potential of these inhibitors and could also be helpful for the future inhibitor design.

The purpose of this study was to analyze the interaction patterns of elementary English classes in terms of the taking of turns, number of words used,...

This paper presents an integrated view on a series of experiments conducted with an affective dialog system, applied as a tool in studies of emotions and social processes in online communication. The different realizations of the system are evaluated in three experimental setups to verify effects of affective profiles, as well as of fine-grained communication scenarios on users' expressions of affective states, experienced emotional changes, and interaction patterns. Results demonstrate that the system applied in virtual reality settings matches a Wizard-of-Oz in terms of chatting enjoyment, dialog coherence, and realism. Variants of the system's affective profile significantly influence the rating of chatting enjoyment and an emotional connection. Self-reported emotional changes experienced by participants during an interaction with the system are in line with the type of applied profile. Analysis of interaction patterns, i.e., usage of particular dialog act classes, word categories, and textual expressions of affective states for different scenarios, demonstrates that a communication scenario for social sharing of emotions was successfully established. The experimental evidence provides valuable input for applications of affective dialog systems and strengthens them as valid tools for studying affect and social aspects in online communication.

Assessment of Shopping Behavior. Automatic System for Behavioral Cues Analysis

We introduce a new architecture for e-textbooks which contains two navigational aids: an index and a concept map. We report results from an evaluation in a university setting with 99 students. The interaction sequences of the users were captured during the user study. We found several clusters of user interaction types in our data. Three separate user types were identified based on the interaction sequences: passive user, term clicker, and concept map user. We also discovered that with the concept map interface users started to interact with the application significantly sooner than with the index interface. Overall, our findings suggest that analysis of interaction patterns allows deeper insights into the use of e-textbooks than is afforded by summative evaluation.

An analysis on interaction patterns and scaffolding techniques is attempted on an online discussion forum happening in 10 groups of 36 students in an Engineering Mathematics unit. The analysis was performed on the content of the student and moderator postings and classification done based on the IBIS model. It was noticed that a lot of student interactions were related to students proposing issues and their positions. Majority of the moderator interactions were Socratic questioning modes and suggestions to effectively coordinate with peer members. The scaffold of Socratic questioning was seen to contribute to the active responses in the online groups.