15133 research outputs found
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Halonen, Pekka - Children reading
No full texthttps://digitalcommons.montclair.edu/iapc_thinking_gallery/1017/thumbnail.jp
\u3cem\u3eWhat is a River?\u3c/em\u3e (2021) by Monika Vaicenavičiene
Full text linkMonika Vaicenavičienė’s lush picture book What is a River? nudges us to contemplate our interconnection with living things and natural cycles, and with human history and culture. It relates a conversation between a grandmother and a young adult grandchild enjoying a picnic on a riverbank. Noticing that the river’s surface reflection of trees and flowers along its banks hides its lower depths (“Just like people”), the grandchild asks, “Grandma, what is a river?” The grandmother’s many responses do more than help her grandchild appreciate multiple meanings and values of rivers; they demonstrate how interesting some things become if we keep asking, “Is there anything else going on here?” or, in the grandmother\u27s language, What else is a river?”https://digitalcommons.montclair.edu/iapc_thinkingstories_picturebooks/1039/thumbnail.jp
Analysis of Popular Songs in the US Market
Full text linkIn today’s digitally driven music landscape, understanding what drive’s a song’s popularity requires insight not only into its acoustic and lyrical content, but also into patterns of listener engagement across platforms. This thesis explores the predictive and descriptive dimensions of song popularity by applying supervised and unsupervised machine learning models to a multi-source dataset integrating audio features, sentiment analysis, and temporal consumption behavior. Drawing from a novel, multi-platform dataset that includes Billboard Hot 100 rankings, Spotify acoustic features and popularity scores, streaming, airplay, and sales metrics as reported on Luminate’s Music Connect, and lyrics from AZLyrics, the study investigates the relationships between musical structure, listener behavior, and popularity outcomes. Our research proceeds using predictive methods. Supervised learning models including Random Forest classifiers and regressors are employed to predict user engagement features that contribute the most to classifying core genre (as defined by Billboard), predict user engagement, and determine which features contribute the most to predicting artists generating the highest volume of digital song sales. This research also employs XGBoost and feature construction to determine user engagement influencing Spotify’s proprietary popularity score. Unsupervised learning techniques such as Principal Component Analysis and K-Means clustering are used to identify latent groupings of songs based on audio attributes. A methodological innovation we employ involves the application of the TSFresh package to extract hundreds of time-series features from weekly streaming data, enabling a detailed examination of how popularity evolves over time. Results from this show that temporal consumption behavior does have significant predictive power. Specifically, on-demand audio streaming contributes significantly to predicting popularity. This is a novel finding not reported in prior studies. In contrast, streamed video adds little value to popularity prediction, which in turn improves the efficiency of the classification model by reducing the overhead associated with collecting this additional feature. Our results show support that using a combined dataset of user engagement and audio features performs the best. Prediction accuracy from our model was 0.84 using the combined features. However, we found that genre is not a useful indicator for predicting user engagement. Research also finds support for features contributing the most to digital sales and the impact of user engagement on song popularity. These include aggregated prior sales at the artist level, streaming on demand audio, airplay, streaming programmed audio, popularity score, and genre. Our results also reveal that time-series dynamics, particularly streaming volatility and structured growth patterns, offer strong predictive value. While audio characteristics like energy, tempo, and valence help explain clustering patterns, popularity is shown to be more closely tied to the temporal structure of listener engagement. Sentiment analysis on a subset of lyrics provides additional context but is limited by data sparsity. This study contributes to the growing field of music analytics by presenting a hybrid framework that combines content-based features with behavioral consumption data. It offers practical implications for music recommendation systems, marketing strategies, and artist development. At the same time, advancing academic understanding of how popularity emerges and sustains in the modern music economy
Lipman Sharp 1980s
No full texthttps://digitalcommons.montclair.edu/iapc_amsharp_gallery/1005/thumbnail.jp
Why Gen Z is More Anxious than Ever? Turning to Social Chats for Answers
Full text linkAs social media has evolved into a prominent part of Gen Z’s social interaction, this study attempted to enrich our current understanding of Gen Z’s mental health struggles through their conversations surrounding anxiety on social media. Highlights of the findings included the following. First, the overwhelming presence of fear (83%) illustrates that anxiety is an emotionally draining experience for many Gen Z users. The use of emotionally charged language to publicly share their struggles with anxiety reflect increasing self-awareness and a cultural shift toward normalizing mental health conversations. Second, the open, candid conversations on anxiety indicate that anxiety is experienced as a threat to many Gen Z’s basic life functioning, from financial security to work performance to daily interactions. Third, the viral praises of Doechii’s “Anxiety” and creative fan adaptations such as Joetastic’s “Her Anxiety” illuminate the power of pop culture and hashtag communities to foster meaningful dialogues on mental health
An empirical study on impact of label noise on synthetic tabular data generation
Full text linkSynthetic data has been actively used for various machine learning-based tasks due to its benefits such as massive reproducibility and privacy enhancement compared to using the original data. The quality of the generated synthetic dataset crucially depends on the quality of the original data, and the latter is often corrupted by label noise. While there have been studies on feature noise, how label noise affects synthetic data generation is under-explored. In this paper, we evaluate the impact of the noisy label on synthetic data generation with a focus on tabular data. One challenge is how to evaluate the quality of synthetic data under label noise. To this end, we design comprehensive experiments to measure the impact of label noise on synthetic data generation in different aspects: synthetic data quality, data utility, and convergence for training synthesizers and machine learning models for downstream tasks. The empirical results cover wide aspects of synthetic data generation under label noise and they show quality and utility degrades with higher noise levels while there is no significant effect on the synthesizer convergence observed
\u3cem\u3eBoy Whose Head was Filled with Stars: A Life of Edwin Hubble\u3c/em\u3e (2021) by Isabelle Marinov
Full text linkBarely a hundred years ago, Edwin Hubble changed the way we understand the universe and our place in it more dramatically than almost anyone else has ever done. As this picture book biography shows, his mind-boggling discoveries can be traced back to childhood questions. The discovery that the world is bigger than we had imagined happens in many ways, and learning that we have some choices in deciding how new facts, new experiences, and new perspectives can matter to us is a necessary part of education.https://digitalcommons.montclair.edu/iapc_thinkingstories_picturebooks/1013/thumbnail.jp
Giant tree versus iPad kids: Reflecting on childhood outdoor play, then and now.
Full text linkChildhood play experiences, particularly outdoor play, positively affect children’s holistic development and well-being. This qualitative study examined teacher education students’ perceptions through retrospective reflection on their own childhood play experiences in the past (then) compared to contemporary children’s play at present (now) to recognize the value of childhood play. Ten student teachers enrolled in various teacher education programs at one university participated in this study. Analyzing a drawing task and interviews revealed that participants recalled outdoor play as their favorite childhood play experiences, engaging in unstructured and child-initiated activities, taking risks, and creating their own rules. These outdoor experiences not only fostered social interactions but also nurtured a special emotional bond with the natural environment where they grew up and played. Furthermore, participants reported that children’s play has changed in recent times, with a decline in outdoor play due to increased parental safety concerns and the prevalence of technological devices. The teaching implications regarding prioritizing outdoor play, as well as the intersection of outdoor play and digital technology, are discussed