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Two studies investigated change in computer science (CS) students' implicit intelligence beliefs. Across both studies, we found that the strength of incremental and entity beliefs changed across time. In Study 1, we found that incremental beliefs decreased and entity beliefs increased across the semester. Change in implicit intelligence beliefs was similar for students taking introductory and upper-division courses. In Study 2, growth curve analysis revealed a small linear change in incremental beliefs across time but no change in entity beliefs--these trends were similar for students enrolled in introductory and upper-division CS courses. Across both studies, change in implicit intelligence beliefs was not associated with academic achievement in CS. Findings provide preliminary evidence that shifts in implicit intelligence beliefs occur as students progress through the CS curriculum. Finally, findings support that mindset interventions may be more effective if delivered at the beginning of the semester before shifts in beliefs occur.

Contribution: This article presents a synthesis of the findings and implications from the IC2Think program of research in undergraduate computer science (CS) courses examining student motivation and self-regulated learning (SRL). These studies illuminate both the difficulty and potential for motivating CS students, as well as the uniqueness of CS as a context for studying undergraduate motivation. Background: Computing disciplines are increasingly important in preparing the future workforce. It is imperative that CS educators understand how to motivate students and enhance student outcomes. Synthesizing findings across multiple studies allows for the emergence of new insights into student motivation and SRL. Research Questions: Which aspects of students' motivation and SRL are predictive of achievement and retention in CS and how can findings inform CS education? Methodology: The primary methodology is a comprehensive review of seven years of research on undergraduate CS education. Studies use a variety of analysis techniques, examine a range of constructs, and include multiple introductory and advanced CS courses. Studies of relationships between variables and change over time were conducted. Findings: The present synthesis of studies on motivation and SRL highlights the complex, counter-intuitive, and positive aspects of student motivation in CS.

Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying which of the many variables derived from tracked data are useful for predicting student learning. This challenge has prompted considerable research in the field of educational data mining and learning analytics. This work advances such research in four ways. First, we bring together two approaches for finding salient variables from separate research areas: hierarchical linear modeling (HLM) from education and Lasso feature selection from computer science. Second, we show that these two approaches have complimentary and synergistic results with some variables considers salient by both and others salient by only one. Third, and most importantly, we demonstrate the benefits of a combined approach that considers a variable salient when either HLM or Lasso consider that variable salient. This combined approach both improves model predictive accuracy and finds additional variables considered salient in previous datasets on student learning. Lastly, we use the results to provide insights into the salient variables to the learning outcome in undergraduate CS education. Overall, this work suggests a combined approach that improves the identification of salient variables in big data and also improves the design of LO tracking systems for learning management systems.

Contribution: This paper provides evidence that computational creativity exercises (CCEs) can increase engineering students' learning in introductory computer science (CS1) courses. Its main contribution is its more rigorous treatment/control group research design that allows testing for causal influences of CCEs on student learning and performance. Background: Computer science (CS) courses are critical foundational courses for engineering students. CCEs that merge computational and creative thinking have been shown to increase achievement and learning of engineering and nonengineering students in CS1 courses, but previous research has used quasi- and non-experimental designs. Intended Outcomes: CCEs are intended to improve students' learning of CS1 content and problem-solving ability by fostering computational creativity. Application Design: CCEs can improve student learning and can be used to supplement other evidence-based instructional practices. Findings: Propensity score matching was used to create equivalent treatment and control groups; results show that students in the CCE implementation section had higher scores on a CS knowledge test than students in the control section, but not higher self-efficacy for their CS knowledge. Focus group and open-ended survey questions indicated that students had mixed reactions to the CCEs, with about half the students seeing them as improving their learning, understanding, and ability to apply CS in their engineering field. Responses also reinforced the importance of fully incorporating CCEs in courses and aligning them with course topics.

Computational thinking and creative thinking are valuable tools both within and outside of computer science (CS). The goal of the project discussed here is to increase students' achievement in CS courses through a series of computational creativity exercises (CCEs). In this paper, the framework of CCEs is described, and the results of two separate studies on their impact on student achievement are presented. Students in introductory CS courses completed CCEs as part of those courses. Students in Study 1 came from a variety of programs, and students in Study 2 were engineering majors. A profiling approach was used to test whether the impact of the CCEs could be accounted for by differences in students' motivated and self-regulated engagement. Overall, CCEs had positive impacts on students' grades and knowledge test scores, and although there were differences in achievement across the profiles, the impact of the CCEs was generally consistent across profiles. The CCEs appear to be a promising way to increase student achievement in introductory CS courses. Implications and directions for future research are discussed.

Place names facilitate locating and distinguishing geographic space where human activities and natural phenomena occur. Extracting place names at multiple spatial resolutions from text is beneficial in several tasks such as identifying the location of events, enriching gazetteers, discovering connections between events and places, etc. Most modern place name extraction approaches generalize the linguistic rules and lexical features as a universal rule and ignore patterns inherent in place names in the geographic contexts. As a result, they lack spatial awareness to effectively identify place names from different geographic contexts, especially the lesser-known place names. In this research, we develop a novel Spatially-Aware Location Extraction (SALE) algorithm for place name extraction from structured documents that uses a hybrid approach comprising of knowledge-driven and data-driven methods. We build a custom named entity recognition (NER) system based on the conditional random field (CRF) and train/ fine-tune it using spatial features extracted from a dataset based on a given geographic region. SALE uses multiple pathways, including the use of the spatially tuned NER to enhance the efficacy in our place names extraction. The experimental results using a large geographic region show that our algorithm outperforms well-known state-of-the-art place name recognizers. [ABSTRACT FROM AUTHOR]

Background: Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self-regulation constructs. Purpose (Hypothesis): This project sought to determine what motivational and self-regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self-regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed. Design/Method: Five hundred and thirty-eight students, 332 of them engineering majors, were surveyed on motivation and self-regulation variables. Data were analyzed from a learner-centered profile approach using cluster analysis. Results: We obtained a five-cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering-tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not. Conclusions: Findings indicate the motivational and self-regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self-regulated learning scaffolds in the classroom.

The goal of the present study was to utilize a profiling approach to understand differences in motivation and strategic self-regulation among post-secondary STEM students in major versus required non-major computer science courses. Participants were 233 students from required introductory computer science courses (194 men; 35 women; 4 unknown) at a large Midwestern state university. Cluster analysis identified five profiles: (1) a strategic profile of a highly motivated by-any-means good strategy user; (2) a knowledge-building profile of an intrinsically motivated autonomous, mastery-oriented student; (3) a surface learning profile of a utility motivated minimally engaged student; (4) an apathetic profile of an amotivational disengaged student; and (5) a learned helpless profile of a motivated but unable to effectively self-regulate student. Among CS majors and students in courses in their major field, the strategic and knowledge-building profiles were the most prevalent. Among non-CS majors and students in required non-major courses, the learned helpless, surface learning, and apathetic profiles were the most prevalent. Students in the strategic and knowledge-building profiles had significantly higher retention of computational thinking knowledge than students in other profiles. Students in the apathetic and surface learning profiles saw little instrumentality of the course for their future academic and career objectives. Findings show that students in STEM fields taking required computer science courses exhibit the same constellation of motivated strategic self-regulation profiles found in other post-secondary and K-12 settings.

Learning objects (LOs) are digital or non-digital entities used for learning, education or training commonly stored in repositories searchable by their associated metadata. Unfortunately, based on the current standards, such metadata is often missing or incorrectly entered making search difficult or impossible. In this paper, we investigate automating metadata generation for SCORM-complaint LOs based on user interactions with the LO and static information about LOs and users. Our framework, called the Intelligent Learning Object Guide (iLOG), involves real-time tracking of each user sessions (an LO Wrapper), offline data mining to identify key attributes or patterns on how the LOs have been used as well as characteristics of the users (MetaGen), and the selection of these findings as metadata. Mechanisms used in the data mining include data imputation via clustering, association rule mining, and feature selection ensemble. This paper describes the methodology of automatic annotation, presents the results on the evaluation and validation of the algorithms, and discusses the resulting metadata. We have deployed our iLOG implementation for five LOs in introductory computer science topics and collected data for over 1400 sessions. We demonstrate that iLOG successfully tracks user interactions that can be used to automate the generation of meaningful empirical usage metadata for different stakeholder groups including learners and instructors, LO developers, and researchers. (Contains 2 figures and 5 tables.)

Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free riding, student assessment, forming student groups that improve student learning, etc). We have used a multiagent-based system to develop a CSCL application and multiagent-frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and ClassroomWiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that our lessons would allow 1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help to improve the design of such systems, and 2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms. (Contains 5 tables and 3 figures.)

Two critical issues of the typical computer-supported collaborative learning (CSCL) systems are inappropriate selection of student groups and inaccurate assessment of individual contributions of the group members. Inappropriate selection of student groups often leads to ineffective and inefficient collaboration, while inaccurate assessment of individual contributions of the group members (1) hinders healthy working relationships among members and (2) prevents teachers from providing precise interventions to specific students. To address these issues, our proposed iHUCOFS framework forms student groups by balancing the students' competence (what the students know) and compatibility (whom they like as peers) for each group. The competence and compatibility are calculated using the assessment of student contributions derived from a newly implemented asynchronous collaborative writing module's detailed tracking information. Results suggest that: (1) the use iHUCOFS framework may improve: (a) the effectiveness and efficiency of the groups, (b) the perception of the students of their peers and their groups, and (c) the collaboration among students with low and high competence and (2) the teacher can use the detailed information tracked by the collaborative writing module to: (a) improve the design of the CSCL tools and (b) provide precise intervention to improve collaboration among the students. (Contains 14 figures and 6 tables.)

A national web-based survey was administered to 700 undergraduate computer science (CS) programs in the United States as part of a stratified random sample of 797 undergraduate CS programs. The 251 program responses (36% response rate) regarding social and professional issues are presented. This article describes the demographics of the respondents, presents results concerning whether programs teach social and professional issues, how social and professional issues are integrated, perceptions of CS faculty regarding the importance of social and professional issues, pedagogies used to teach social and professional issues, and what specific social and professional topics have been included in the CS curriculum. Additionally, we (a) provide suggestions for CS programs regarding the integration of social and professional issues into the CS curriculum, (b) suggest ways to encourage more social and professional coverage in CS programs, pedagogy, and (c) recommend what social and professional topics should be included in future CS curriculum reports. (Contains 8 tables and 10 figures.)

Affinity Learning is a system that allows the user to build a lesson on a subject matter by breaking it down into concepts, misconceptions, assessments, and remediation steps. Examples and questions can also used in these components. Affinity Learning has been found to be effective and can offer critical insights to student learning strategies. Authoring Affinity Learning lesson plans or hierarchies, however, is non-trivial. We have developed two authoring tools: the first tool provides an overall view of the hierarchy with a "graphical display" of the nodes and links; but the second tool does not. This article reports on a study conducted to test whether the graphical authoring tool can help produce better-quality hierarchies and also help the users learn about the subject matter better than the non-graphical authoring tool. Results show that while the graphical authoring tool can result in better-quality hierarchies, it does not result in better learning. (Contains 10 figures and 4 tables. Appended are: (1) TEAC 451P/851P Technology Project Description; (2) Handout on the Affinity Learning Project; (3) User Feedback; and (4) Test Used in the Study.)

This paper describes the Reinventing Computer Science Curriculum Project at the University of Nebraska-Lincoln. Motivated by rapid and significant changes in the information technology and computing areas, high diversity in student aptitudes, and high dropout rates, the project designed and implemented an integrated instructional/research framework. The framework is based around 10 general design strategies that incorporated administrative, instructional, and research principles. The framework consists of a placement examination, three suites of structured laboratory assignments, multimedia learning objects, and educational evaluation and research designs. The results of implementing the framework in our introductory courses are encouraging and insightful. While validating some of our designs, our research also identified areas for further development and research. (Contains 2 figures and 4 tables.)

An introductory CS1 course presents problems for educators and students due to students' diverse background in programming knowledge and exposure. Students who enroll in CS1 also have different expectations and motivations. Prompted by the curricular guidelines for undergraduate programmes in computer science released in 2001 by the ACM/IEEE, and driven by a departmental project to reinvent the undergraduate computer science and computer engineering curricula at the University of Nebraska-Lincoln, we are currently implementing a series of changes which will improve our introductory courses. One key component of our project is an online placement examination tied to the cognitive domain that assesses student knowledge and intellectual skills. Our placement test is also integrated into a comprehensive educational research design containing a pre- and post-test framework for assessing student learning and providing valuable feedback for needed instructional revisions. In this paper, we focus on the design and implementation of our placement exam and present an analysis of the data collected to date. (Contains 5 tables.)