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Curriculum design is an essential task that is complex, painstaking, thought provoking, and cognitively demanding. Often, educators leave curriculum design up to the “experts,” such as textbook makers, program directors, and curriculum leaders. Although deference to “experts” can be perceived as the more efficient way to approach curriculum design, it removes the power from the instructors to exert their expertise, content knowledge, pedagogical artistry, and ability to address the needs of their specific students. In turn, students’ learning and ultimate generalization and application of that learning may not be fully realized. This chapter seeks to challenge that deference of power and illustrate that curriculum design should be a fundamental component to any course design and implementation. This chapter will illustrate considerations that instructors must keep at the forefront of their thinking when designing curricula; specifically, the provision of relevant content that serves as a basis for sustained and successful employability and addressing diverse student learning needs. This chapter will also provide reasonable, practical frameworks for educators to use to embark on executing this critical component of teaching and learning.

Special education issues and considerations often perplex and confuse many educational institutions, regardless if they are traditional or autonomous organizations such as charters. However, research indicates these issues tend to be more complicated with charters because the realm of special education is highly regulated and in many cases, in direct conflict with charter core tenets of autonomy, innovation, curriculum, and accountability. Since the emergence of charter schools in 1991, researchers have investigated the relationship between charter law and the highly regulated domain of special education. The literature has evolved as charters have become more prevalent and established. But one thing remains the same, charter law and federal regulations are often in conflict with one another and cause great tension for autonomous leaders who strive to improve educational practices and learning for all the students they serve. Thus, this chapter focuses on important leadership considerations when building, improving, and maintaining an effective charter organization with regards to working with students with special needs. Essentially, the tension between autonomous leadership and federal regulations can be eased by planning for students with special needs. The key to successful planning and implementation is through alignment that goes beyond the Interstate School Leaders Licensure Consortium (ISLLC) Standard.

Andrew L. Armagost is a doctoral student in educational leadership at the Pennsylvania State University. He holds a baccalaureate degree in education and policy. His interests in future research include education law, school finance, and teacher employment and certification.

The purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system.

A method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision.

The study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation.

The paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.

Despite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.

This research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.

The empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.

To the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

This study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.

This study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.

The validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.

This study contributes a specific collection for ICTC patents, which is not provided by the patent offices.

The proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.

A deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.