Perspectives on Learning: Methodologies for Exploring Learning Processes and Outcomes

The papers in this Special Issue were initially prepared for an EARLI 2013 Symposium that was designed to examine methodologies in use by researchers from two sister communities, Learning and Instruction and Learning Sciences. The four papers reflect a common ground in advances in conceptions of learning since the early days of the “cognitive revolution” in the 1960s. This commentary shows the interdependence between advances in theory and advances in methodologies. Four shifts in conceptions of learning are described. That these shifts are evident in the work of both communities suggests a blurring of the boundaries between the two

Global Language Variation in Online Writing Instructional Spaces: English as a Lingua Franca Among Global Participants in a Massive Open Online Course

Two vectors of the internationalization of US higher education—online courses and student diversity—intersect at a point where a broad mix of culturally and linguistically diverse students enroll in online courses, including writing courses. This study applies an English as a Lingua Franca (ELF) lens to examine language in an online writing environment in order to understand how the participants use their linguistic resources to communicate in English across varieties and around the world. This study employs discourse analysis to two discussion forums from a US-based composition MOOC (Massive Open Online Course). More than three quarters of the MOOC participants came from outside of North America; almost half reported being native English speakers, and an equal amount reported speaking English enough for most situations. One discussion board centered on the concept of ethos and another centered on brainstorming ideas for the final writing project. In examining how global English language users from a variety of linguistic backgrounds discuss writing in these spaces, this study found that participants expressed understanding and valuing of English language variation across time and geographic locations, and they demonstrated accommodation in use of culturally-laden language forms for the global audience through uses of idioms in the discussion posts. Throughout the forums, deviations from English as a native language (ENL) norms occurred, but in these forum spaces, the flow appears to continue with attention on the communicative goal rather than on the non-ENL variations. These findings evidence strong potential for the inclusion of language awareness activities in US composition instruction spaces. Such work aims to create US university writing courses that are more equitable and effective for a global audience, including helping domestic US students develop important intercultural skills to participate in culturally and linguistically diverse arenas

Sentiment Analysis and Opinion Mining within Social Networks using Konstanz Information Miner

Evaluations, opinions, and sentiments have become very obvious due to rapid emerging interest in ecommerce which is also a significant source of expression of opinions and analysis of sentiment. In this study, a general introduction on sentiment analysis, steps of sentiment analysis, sentiments analysis applications, sentiment analysis research challenges, techniques used for sentiment analysis, etc., were discussed in detail. With these details given, it is hoped that researchers will engage in opinion mining and sentiment analysis research to attain more successes correlated to these issues. The research is based on data input from web services and social networks, including an application that performs such actions. The main aspects of this study are to statistically test and evaluate the major social network websites: In this case Twitter, because it is has rich data source and easy within social networks tools. In this study, firstly a good understanding of sentiment analysis and opinion mining research based on recent trends in the field is provided. Secondly, various aspects of sentiment analysis are explained. Thirdly, various steps of sentiment analysis are introduced. Fourthly, various sentiment analysis, research challenges are discussed. Finally, various techniques used for sentiment analysis are explained and Konstanz Information Miner (KNIME) that can be used as sentiment analysis tool is introduced. For future work, recent machine learning techniques including big data platforms may be proposed for efficient solutions for opinion mining and sentiment analysi

Knowledge modeling of phishing emails

This dissertation investigates whether or not malicious phishing emails are detected better when a meaningful representation of the email bodies is available. The natural language processing theory of Ontological Semantics Technology is used for its ability to model the knowledge representation present in the email messages. Known good and phishing emails were analyzed and their meaning representations fed into machine learning binary classifiers. Unigram language models of the same emails were used as a baseline for comparing the performance of the meaningful data. The end results show how a binary classifier trained on meaningful data is better at detecting phishing emails than a unigram language model binary classifier at least using some of the selected machine learning algorithms

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from vast open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to identify discrepancies between a model's expected responses and its autonomous generation prowess. Through the adept application of IFD, cherry samples are pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on renowned datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of conventional data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the optimization of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/MingLiiii/Cherry_LL

Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems

We can leverage data and complex systems science to better understand society and human nature on a population scale through language --- utilizing tools that include sentiment analysis, machine learning, and data visualization. Data-driven science and the sociotechnical systems that we use every day are enabling a transformation from hypothesis-driven, reductionist methodology to complex systems sciences. Namely, the emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, with profound implications for our understanding of human behavior. Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture\u27s evolution through its texts using a big data lens. Given the growing assortment of sentiment measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large enough portion of a given text\u27s lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories, forming patterns that are meaningful to us. By classifying the emotional arcs for a filtered subset of 4,803 stories from Project Gutenberg\u27s fiction collection, we find a set of six core trajectories which form the building blocks of complex narratives. We strengthen our findings by separately applying optimization, linear decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads. Within stories lie the core values of social behavior, rich with both strategies and proper protocol, which we can begin to study more broadly and systematically as a true reflection of culture. Of profound scientific interest will be the degree to which we can eventually understand the full landscape of human stories, and data driven approaches will play a crucial role. Finally, we utilize web-scale data from Twitter to study the limits of what social data can tell us about public health, mental illness, discourse around the protest movement of #BlackLivesMatter, discourse around climate change, and hidden networks. We conclude with a review of published works in complex systems that separately analyze charitable donations, the happiness of words in 10 languages, 100 years of daily temperature data across the United States, and Australian Rules Football games