Analysis Of Interaction Patterns Research Articles (Page 1)

Purpose This study aims to develop and apply a systems-based analytical methodology for understanding the complexity of learner-content interactions in Massive Open Online Courses (MOOCs). Traditional linear analysis methods primarily focus on predictive modeling for learning outcomes but fail to capture the underlying patterns and dynamic nature of learning interactions. The research seeks to identify behavioral patterns associated with successful learning outcomes and provide insights into how learners construct and reconstruct their interaction patterns in online learning environments. Design/methodology/approach The study employed the ladderpath methodology to analyze over 435,000 log entries from 363 participants in an open online course. Interaction complexity was quantified through two key metrics: ladderpath-index (representing meaningful interaction modules) and order-index (representing interaction quantity). The approach leverages information theory principles to characterize interaction behaviors on a large scale, focusing on the hierarchical organization and reuse of behavioral components rather than simple sequential patterns. Findings Results reveal that successful learners who earned certificates exhibited higher overall interaction quantities, more consistent interaction patterns and less reliance on exploratory behaviors than unsuccessful learners. Specifically, successful learners demonstrated lower ladderpath-rates and higher order-rates, indicating a strategic balance between exploration and routine patterns. These findings challenge assumptions that more varied interaction necessarily leads to better outcomes, suggesting instead that structured and strategic interaction patterns are key to learning success in MOOCs. Originality/value This study introduces a novel, universally applicable method for analyzing learner-content interaction in online learning environments. The ladderpath approach offers a unique perspective by quantifying interaction complexity through information theory, enabling large-scale characterization of learning behaviors. Unlike traditional methods that often require context-specific frameworks, this approach has broader implications for open online learning across various courses and topics. By providing a systemic view of interaction complexity, the study contributes to the field of social physics in education, potentially uncovering fundamental patterns in online learning. This research paves the way for more comprehensive and generalizable analyses of learner behaviors in diverse online educational settings.

The study of dramatic plays has long relied on qualitative methods to analyze character interactions, making little assumption about the structural patterns of communication involved. Our approach bridges NLP and literary studies, enabling scalable, data-driven analysis of interaction patterns and power structures in drama. We propose a novel method to supplement addressee identification in tragedies using Large Language Models (LLMs). Unlike conventional Social Network Analysis (SNA) approaches, which often diminish dialogue dynamics by relying on co-occurrence or adjacency heuristics, our LLM-based method accurately records directed speech acts, joint addresses, and listener interactions. In a preliminary evaluation of an annotated multilingual dataset of 14 scenes from nine plays in four languages, our top-performing LLM (i.e., Llama3.3-70B) achieved an F1-score of 88.75% (P = 94.81%, R = 84.72%), an exact match of 77.31%, and an 86.97% partial match with human annotations, where partial match indicates any overlap between predicted and annotated receiver lists. Through automatic extraction of speaker–addressee relations, our method provides preliminary evidence for the potential scalability of SNA for literary analyses, as well as insights into power relations, influence, and isolation of characters in tragedies, which we further visualize by rendering social network graphs.

This article explores the trends in online learning and the influence of specific criteria in the selection of online courses among executives and entrepreneurs in the business sector. The use of artificial intelligence (AI) in online learning platforms has been a key factor in enhancing the personalization and adaptability of courses, resulting in higher student satisfaction and success. The study highlights the evolution and importance of online learning platforms, detailing their methods, popular courses, pricing, and flexibility, which contribute to their success in virtual education. Additionally, the integration of AI technologies has allowed for a deeper analysis of student interaction patterns, facilitating the identification of areas for improvement and the implementation of personalized interventions. The research underscores the need for continuous updates in both technological tools and educator skills to meet the growing demands of online education. The article concludes that virtual education has a scalable and continuously growing future, driven by its perceived benefits and the adoption of advanced technologies.

ABSTRACT 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.

The global impact of the COVID-19 crisis has underscored the need for novel therapeutic candidates capable of efficiently targeting essential viral proteins. Existing therapeutic strategies continue to encounter limitations such as reduced efficacy against emerging variants, safety concerns, and suboptimal pharmacodynamics, which emphasize the potential of natural-origin compounds as supportive agents with immunomodulatory, anti-inflammatory, and antioxidant benefits. The present study significantly advances prior molecular docking research through comprehensive virtual screening of structurally related analogs derived from antiviral phytochemicals. These compounds were evaluated specifically against the SARS-CoV-2 main protease (3CLpro) and papain-like protease (PLpro). Utilizing chemical similarity algorithms via the ChEMBL database, over 600 candidate molecules were retrieved and subjected to automated docking, interaction pattern analysis, and comprehensive ADMET profiling. Several analogs showed enhanced binding scores relative to their parent scaffolds, with CHEMBL1720210 (a shogaol-derived analog) demonstrating strong interaction with PLpro (−9.34 kcal/mol), and CHEMBL1495225 (a 6-gingerol derivative) showing high affinity for 3CLpro (−8.04 kcal/mol). Molecular interaction analysis revealed that CHEMBL1720210 forms hydrogen bonds with key PLpro residues including GLY163, LEU162, GLN269, TYR265, and TYR273, complemented by hydrophobic interactions with TYR268 and PRO248. CHEMBL1495225 establishes multiple hydrogen bonds with the 3CLpro residues ASP197, ARG131, TYR239, LEU272, and GLY195, along with hydrophobic contacts with LEU287. Gene expression predictions via DIGEP-Pred indicated that the top-ranked compounds could influence biological pathways linked to inflammation and oxidative stress, processes implicated in COVID-19’s pathology. Notably, CHEMBL4069090 emerged as a lead compound with favorable drug-likeness and predicted binding to PLpro. Overall, the applied in silico framework facilitated the rational prioritization of bioactive analogs with promising pharmacological profiles, supporting their advancement toward experimental validation and therapeutic exploration against SARS-CoV-2.

The diverse chemical components of traditional Chinese medicine (TCM) exhibit significant therapeutic potential; however, the action mechanisms of these compounds often remain unclear. The use of drug-target prediction can aid in identifying the specific targets of TCM, thereby revealing their bioactivity and mechanisms. The efficiency, cost-effectiveness, and powerful predictive capabilities of artificial intelligence algorithms have led to their emergence as effective tools for accelerating drug-target interaction analysis. To systematically investigate TCM interaction mechanisms, we integrated cosine‑correlation and similarity‑comparison of local network (CSLN) and molecular dynamics (MD) simulations. The CSLN algorithm predicts that 11-beta-hydroxysteroid dehydrogenase-1 (HSD11B1) serves as a common target for the synergistic effects of triptolide (TP) and glycyrrhizic acid (GA). MD simulations indicate that both TP and GA can maintain stable interactions with HSD11B1 and form a common binding hot region. Surface plasmon resonance (SPR) experiments reveal that both TP and GA can effectively bind to HSD11B1, with binding constants of 29.21μM and 31.75μM, respectively. When used in combination, the binding constant is 5.74μM. The combination of CSLN and MD simulations represents an effective tool for the initial analysis and simulation of interaction patterns between TCM and their targets at the computational level. These findings enhance our understanding of the interaction mechanisms between drugs.

This study analyzes the cultural dynamics that shape consumer behavior in the context of social commerce in the Iranian capital of Tehran. Our sociological investigation examines the influence of norms, values, and cultural perceptions on social media interactions on platforms such as Instagram, Telegram, and internal networks like Soroush and Beel. Using factor analysis and multiple linear regression, we examine the relationships between ten key variables, revealing three main factors—the link of cultural perception, the set of transaction dynamics, and the quadruple of market values—that are primarily responsible for creating the complex interactions between culture and social business behaviors in this context. The findings suggest that cultural attitudes significantly influence consumers’ perceptions of technology, trust in online platforms, and their purchase decisions. Furthermore, cultural values related to product quality, sustainability, and ethical consumption play an important role in shaping consumer expectations and business strategies. Drawing on Baudrillard’s theory of consumption, this study shows that consumer behavior in social commerce goes beyond functional needs, such that purchase decisions are increasingly linked to identity formation and social differentiation. We conclude with recommendations for adapting business strategies to align with cultural preferences, as well as suggestions for future research focused on technological developments and cross-cultural comparisons in social business.

A neurological and developmental ailment that impacts children's behavioral and intellectual development is known as autism spectrum disorder (ASD). It frequently results in recurrent habits, narrow interests, communication problems, and trouble interacting with others. ASD's severity and long-term repercussions can be reduced with an early diagnosis. Traditional diagnostic methods are subjective, time-consuming, and require specialized expertise, leading to delays in intervention. The paper addresses the challenge of accurately diagnosing Autism Spectrum Disorder (ASD) in children. The aim is to develop an automated system using machine learning to predict and analyze the autistic children. By collecting and analyzing behavioral, cognitive, and physiological data, the system will identify key features that distinguish ASD. Multiple machine learning models will be developed and evaluated for accuracy and reliability. The final outcome will be a user-friendly diagnostic tool to assist healthcare professionals in early and precise ASD detection, improving early intervention and developmental outcomes.

Analysis of English learning community interaction patterns in social networks based on knowledge graphs

BackgroundAlzheimer disease and related dementias (ADRD) are a growing global health challenge. ADRD place significant physical, emotional, and financial burdens on informal caregivers and negatively affects their well-being. Web-based social media platforms have emerged as valuable sources of peer support for these caregivers. However, there has been limited investigation into how web-based peer support might influence their mental well-being.ObjectiveThis study aims to examine the dynamics of sentiment scores, a major indicator of mental well-being, among informal ADRD caregivers, specifically how their sentiment changes as they participate in caregiving experience discussions within 2 ADRD web-based communities.MethodsWe collected data from 2 large web-based ADRD caregiving communities, ALZConnected (from November 2011 to August 2022) and TalkingPoint (from March 2003 to November 2022). Using the Valence Aware Dictionary for Sentiment Reasoning and Linguistic Inquiry and Word Count, we calculated sentiment scores for each post and evaluated how the initial sentiment score of a topic initiator evolves within a discussion thread. Structured topic modeling and regression analysis were used to identify the primary topics consistently associated with sentiment changes within these threads. We investigated longitudinal sentiment trends to identify patterns of sentimental stability or enhancement due to prolonged engagement in web-based communities by plotting linear interpolation lines of the sentiment values of each individual user.ResultsThe ALZConnected dataset comprised 532,992 posts, consisting of 57,641 topic threads and 475,351 comments. The TalkingPoint dataset was composed of 846,344 posts, consisting of 81,068 topic threads and 765,276 comments. Our research revealed that topic initiators experienced a notable increase in sentiment as they engaged in subsequent discussions within their threads, with a significant uptick in positivity in the short term. This phenomenon is part of a broader trend of steadily rising positive sentiment among ADRD caregivers. Using structured topic modeling, we cataloged a diverse range of topics that included both emotional aspects, such as family emotions, and practical concerns, such as diagnosis and treatment and everyday care practices. We observed that sentiment scores were positively aligned with discussions about family and daily routines life (coefficient=3.53; P<.001), while topics related to illness (coefficient=–1.37; P<.001) and caregiving facilities (coefficient=–1.98; P<.001) tended to correlate with lower sentiment scores. This evidence highlights the significant impact that both the time of participation and the posting content have on the sentiment changes of caregivers.ConclusionsThis study identifies sentiment changes among informal ADRD caregivers through their interactions in 2 extensive web-based communities. These findings emphasize the importance of early emotional support within a topic thread and demonstrate a predominantly positive sentiment in these communities over time. These further highlight the value of web-based peer support and its potential to enhance the emotional well-being of informal ADRD caregivers.

The dynamic, reciprocal, and bidirectional relationships in encounters between infants and their caregivers are called early interactions. Evidence shows that these interactions influence cognitive, emotional, and social development beyond the early years. While some studies have examined these interactions in dyads with infants with Down syndrome, they have mostly focused on parents in small samples. This study explores these interactions by considering parental, infant, and interaction variables. A total of 128 dyads participated, with 64 infants with Down syndrome and 64 typically developing infants, matched one-by-one by developmental age. During home visits, socio-demographic and developmental information was collected, development and dyadic interactions were assessed using standardised instruments. Descriptive analyses, MANOVAs, and ANOVAs were conducted comparing the group of dyads that included infants and toddler with Down syndrome and those with typical development. Infant and toddler gender showed significant differences and was included as a relevant factor in the analyses. Key findings include lower scores in parental sensitivity and non-directiveness in dyads with children with Down syndrome. Children with Down syndrome also showed lower scores in attention to the caregiver. Interactions with children with Down syndrome exhibited less mutuality and engagement. Significant gender-based interactions were found, showing that parents are more sensitive and less directive with girls with Down syndrome, who also show greater expression of negative affect and better attention to the caregiver. This study suggests different qualities in early interactions when a child with Down syndrome is involved. These interactions are characterised by lower sensitivity and greater directiveness, possibly in response to the lower attention towards the caregiver observed in these children. This results in less mutual interaction. The findings' alignment with previous research and implications for clinical work are discussed. Given the observed effect of the child's gender, future research should further explore this aspect.

Identifying therapeutic inhibitors of crucial enzymes involved in the peptidoglycan biosynthesis pathway is pivotal for developing new treatments against multidrug-resistant Enterococcus faecalis V583. MurM, an essential enzyme in this pathway, plays a significant role in the bacterium's cell wall synthesis, making it an attractive druggable target for novel antimicrobial strategies. This study explored the potential of natural compounds as inhibitors of MurM, aiming to discover promising drug candidates that could serve as the foundation for future therapeutic development. The three-dimensional structure of MurM was predicted, optimized, and its binding pocket was analyzed by comparing it with related structures. Over 4,70,000 natural compounds from the COCONUT database were subjected to virtual high-throughput screening (vHTS). The top lead candidates were selected based on their Lipinski's profile, ADME profile, toxicity profile, estimated binding free energy (ΔG) and estimated inhibition constant (Ki). Interaction pattern analysis was used to evaluate the non-covalent interactions between the inhibitors and key residues in MurM's binding pocket. Molecular dynamics simulations were performed over 300 ns to assess the structural stability and impact of these inhibitors on MurM's enzyme. Three lead compounds-CNP0056520, CNP0126952, and CNP0248480-were identified and prioritized with estimated ΔG ranging from - 9.35 to -7.9kcal/mol. Molecular dynamics simulations revealed minimal impact on MurM's overall structure and dynamics, with the candidate inhibitors forming stable protein-ligand complexes. These interactions were supported by several non-covalent interactions between the candidate inhibitors and key residues within MurM's binding pocket. These findings suggest that the identified natural product candidates could serve as promising inhibitors of MurM, potentially leading to novel therapeutics targeting cell wall biosynthesis in multidrug-resistant E. faecalis.

Unveiling the potential of novel 5α-reductase inhibitors via ligand based drug design, molecular docking and ADME predictions to manage BPH

Becoming more severe at the area and public level should be noticeable as a technique for creating money-related improvement and dealing with people's association help. Subsequently, the Mojokerto Territorial Government should zero in on the circumstance with a nearby advancement extraordinary framework. The control of connecting and resource-based potential in planning is primary for work on the chance of neighbourhood improvement execution. Vigilant arrangements with the heading of nearby improvements end up being more effective. This article will isolate the Advancement of City Checking Soul of Majapahit in Associating with the Creative Money-related Progress of MSMEs in Mojokerto City. This assessment uses an exciting evaluation, where data is taken from the working environment of cooperatives, MSMEs, industry, and trade in Mojokerto City. The triangulation analysis method used in this research and Miles and Matthew’s interaction pattern analysis, which takes place continuously through collecting, reducing, presenting data, and conclusions, will ensure good data generation. This study shows that the success of the Mojokerto City Government in building a creative economy with local innovation and carrying the "Spirit of Majapahit" branding has a positive impact on the growth of the creative economy because local MSMEs contribute by producing a variety of interesting products that support the "Spirit of Majapahit" branding ranging from consumption needs to handicrafts.Becoming more severe at the area and public level should be noticeable as a technique for creating money-related improvement and dealing with people's association help. Subsequently, the Mojokerto Territorial Government should zero in on the circumstance with a nearby advancement extraordinary framework. The control of connecting and resource-based potential in planning is primary for work on the chance of neighbourhood improvement execution. Vigilant arrangements with the heading of nearby improvements end up being more effective. This article will isolate the Advancement of City Checking Soul of Majapahit in Associating with the Creative Money-related Progress of MSMEs in Mojokerto City. This assessment uses an exciting evaluation, where data is taken from the working environment of cooperatives, MSMEs, industry, and trade in Mojokerto City. The triangulation analysis method used in this research and Miles and Matthew’s interaction pattern analysis, which takes place continuously through collecting, reducing, presenting data, and conclusions, will ensure good data generation. This study shows that the success of the Mojokerto City Government in building a creative economy with local innovation and carrying the "Spirit of Majapahit" branding has a positive impact on the growth of the creative economy because local MSMEs contribute by producing a variety of interesting products that support the "Spirit of Majapahit" branding ranging from consumption needs to handicrafts.

In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors. We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

In recent years, the gaming industry has witnessed a remarkable evolution, with open-world games emerging as a dominant genre that captivates players with expansive virtual worlds, intricate narratives, and immersive gameplay experiences. Among these innovative titles, "City of Dreams" stands out as a pinnacle of open-world game design, offering players a richly detailed urban landscape to explore, inhabit, and shape according to their desires. This research paper provides a comprehensive analysis of "City of Dreams," examining its technical development, player engagement dynamics, societal impact, and broader implications within the gaming industry and digital culture. The technical development of "City of Dreams" is explored in detail, encompassing various aspects such as game engine selection, character animation, environment design, and physics implementation. By delving into the technical intricacies of game development, this research sheds light on the creative processes and technological innovations that underpin the creation of immersive gaming experiences. Furthermore, the paper investigates the player engagement dynamics of "City of Dreams," exploring how its diverse gameplay mechanics, immersive world design, and compelling narrative drive player engagement and immersion. Through an analysis of player behavior, feedback, and interaction patterns, insights are gained into the factors that contribute to the game's success and longevity. Keywords—open-world games, player experience, player behavior, game development, immersive environments, Non-Playable Characters (NPCs).

The implementation of correctional revitalization is not only related to structural adjustments and security protocols at Super Maximum Security (SMAX), Maximum Security (MAX), and Medium Security (MS) correctional facilities; it may also have an impact on changes in officer-inmate interaction patterns and the psychological stress officers. After the implementation of institutional revitalization, this study intends to investigate the patterns of officer-inmate interaction and the levels of stress experienced by correctional institution personnel in Nusakambangan with various degrees of security. Eight officers were interviewed as part of the research approach, which involved descriptive qualitative analysis, in four prisons on Nusakambangan Island. The analysis's findings give an example of the many ways that SMAX, MAX, and MS prison guards and convicts engage with one another. Because of restrictions on prisoners' freedom of movement, the implementation of security measures, and disparities in treatment within the development program, different interaction patterns result. Comparing convicts in XMAX and MAX prisons to those in MS prisons, authorities believe that MS prisoners spend more time outside of their rooms engaging in activities, which could compromise security and order. The result is that because inmates in MS prisons are free to roam the facility, but inmates in SMAX and MAX prisons engage in more activities in their quarters, the stress level for officers in MS prisons is higher than the stress level in SMAX and MAX prisons.

Lung cancer is a disease in which lung cells grow abnormally and uncontrollably, and the cause of it is direct smoking, secondhand smoke, radon, asbestos, and certain chemicals. The worldwide leading cause of death is lung cancer, which is responsible for more than 1.8 million deaths yearly and is expected to rise to 2.2 million by 2030. The most common type of lung cancer is non-small cell lung cancer (NSCLC), which accounts for about 80% and small cell lung cancer (SCLC), which is more aggressive than NSCLC and is often diagnosed later and accounts for 20% of cases. The global concern for lung cancer demands efficient drugs with the slightest chance of developing resistance, and the idea of multitargeted drug designing came up with the solution. In this study, we have performed multitargeted molecular docking studies of Drug Bank compounds with HTVS, SP and XP algorithms followed by MM\GBSA against the four proteins of lung cancer cellular survival and stress responses, which revealed Mitoglitazone as a multitargeted inhibitor with a docking and MM\GBSA score ranging from - 5.784 to - 7.739 kcal/mol and - 25.81 to - 47.65kcal/mol, respectively. Moreover, we performed pharmacokinetics studies and QM-based DFT analysis, showing suitable candidate and interaction pattern analysis revealed the most count of interacting residues was 4GLY, 5PHE, 6ASP, 6GLU, 6LYS, and 6THR. Further, the results were validated with SPC water model-based MD simulation for 100ns in neutralised condition, showing the cumulative deviation and fluctuation < 2Å with many intermolecular interactions. The whole analysis has suggested that Mitoglitazone can be used as a multitargeted inhibitor against lung cancer-however, experimental studies are needed before human use.

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.

Effective teaching is a fundamental component of quality education. Educational institutions and stakeholders are constantly seeking ways to assess and enhance teaching practices to ensure optimal learning outcomes for students. This paper provides an overview of the various approaches and methods used for assessing effective teaching. It delves into the significance of aligning assessment strategies with specific learning goals and discusses the importance of considering both quantitative and qualitative measures. The paper highlights the role of student feedback in evaluating teaching effectiveness. It explores the use of surveys and evaluations to gather insights into instructional methods, classroom management, and the overall learning experience. Additionally, the incorporation of peer evaluations and self-assessment by instructors is examined as valuable tools for comprehensive teaching assessment. Furthermore, the paper addresses the growing influence of technology in teaching assessment. The utilization of learning analytics and data-driven insights offers a new dimension to evaluating teaching effectiveness. This includes the analysis of student performance data, engagement metrics, and online interaction patterns. It involves evaluating and understanding the various factors that contribute to effective teaching and its impact on student learning outcomes. Student Learning Outcomes: Effective teaching directly influences student learning outcomes. Research in this area helps identify teaching practices that lead to improved academic achievement, critical thinking skills, problem-solving abilities, and overall educational success. Educational Quality: High-quality teaching is a cornerstone of a strong education system. Understanding what makes teaching effective enables educational institutions to provide a better learning experience, attract and retain skilled educators, and enhance their reputation for producing successful graduates. TOPSIS, This method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. Alternative taken as Student Evaluations, Peer Reviews, Self-Assessment, Classroom Observations Evaluation preference taken as Enhanced Learning Outcomes, Continuous Improvement, Subjective Bias, Time-Consuming. From the result Student performance data is got the first rank and classroom observation is having the lowest rank.