A Model Student Personalized Education Plan into a Portfolio through the Use of Career Pathways in South Kitsap High School

The purpose of this project was to create a Personalized Education Plan and Portfolio system to support secondary students to acquire the skills necessary to ensure employment in South Kitsap High School Port Orchard, Washington. To accomplish this purpose, current research and information related to Personalized Education Plans and Portfolios were reviewed. Additionally, career pathways were established, personal data, resume, individual career planning portfolio, and career goals inventory review worksheets were adapted and developed

Factual and Personalized Recommendations using Language Models and Reinforcement Learning

Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPelling, Precise, Personalized, Preference-relevant language model (P4LM) that recommends items to users while putting emphasis on explaining item characteristics and their relevance. P4LM uses the embedding space representation of a user's preferences to generate compelling responses that are factually-grounded and relevant w.r.t. the user's preferences. Moreover, we develop a joint reward function that measures precision, appeal, and personalization, which we use as AI-based feedback in a reinforcement learning-based language model framework. Using the MovieLens 25M dataset, we demonstrate that P4LM delivers compelling, personalized movie narratives to users

Tidying Up the Conversational Recommender Systems' Biases

The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models

How Does the Implementation of Social Entrepreneurship Business-to-Business Marketing Strategy?

XYZ as a social entrepreneur in Bogor, produces handicrafts from newspapers to raise public awareness of environmental and social issues. This study aims to identify the marketing mix applied by XYZ, analyze customer perceptions of the marketing mix, and formulate recommendations for XYZ's marketing mix 4.0 to increase demand for newspaper craft products. Convenience sampling was used, and descriptive data analysis was performed. The respondents included three internal company representatives, 13 customers, and 12 prospective customers. According to the research findings, XYZ successfully implemented a 4P marketing mix strategy that covered product, price, place/distribution, and promotion. XYZ's marketing strategy in these areas has been rated highly in the excellent category of product (100%), price (85%), place/distribution (77%), and promotion (92%). However, the research also suggests that a company could enhance its marketing tactics by adopting the 4C marketing mix (co-creation, currency, communal activation, and conversation) based on insights gathered from customers and potential customers to better cater to the market's needs

Adaptive and Re-adaptive Pedagogies in Higher Education: A Comparative, Longitudinal Study of Their Impact on Professional Competence Development across Diverse Curricula

This study addresses concerns that traditional, lecture-based teaching methods may not sufficiently develop the integrated competencies demanded by modern professional practice. A disconnect exists between conventional pedagogy and desired learning outcomes, prompting increased interest in innovative, student-centered instructional models tailored to competence growth. Despite this, nuanced differences in competence development across diverse university curricula remain underexplored, with research predominantly relying on students’ self-assessments. To address these gaps, this study employs longitudinal mixed-methods approaches with regard to theory triangulation and investigator triangulation to better understand how professional knowledge, skills, and dispositions evolve across varied curricula and contexts. This research emphasizes adaptive and re-adaptive teaching approaches incorporating technology, individualization, and experiential learning, which may uniquely integrate skill development with contextual conceptual learning. Specific attention is paid to professional education paths like design, media, and communications degrees, where contemporary competence models stress capabilities beyond core conceptual knowledge. Results from this study aim to guide reform efforts to optimize professional competence development across diverse academic areas

Text Is All You Need: Learning Language Representations for Sequential Recommendation

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings.Comment: accepted to KDD 202