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This paper explores the impact of feminist scholarship on the professions of education and business, and looks critically at the assumptions on which the study of professionalism has been based. The paper begins with a feminist critique of professionalism, based on characteristics of professions and gender theory. Feminist theory is applied to education, focusing on research, textbooks, curriculum, gender bias, and efforts to empower teachers and students. Feminist critiques of research on teaching and learning are examined, using gender as a theoretical framework through which to critique male-dominated theories, reconceptualize teaching and learning, and restructure the educational system. Feminist scholarship on business is then addressed, and its limitation to the liberal perspective is noted. Three kinds of critiques are explored: research examining gender as a regulator of individuals' activity according to their biological sex, research on the cult of true womanhood as opposed to the cult of rationality, and research on the changing character of American business. It is concluded that as feminists begin to question the professionalism paradigm and to subject it to gender analysis, models can be built that are more sensitive to women as professionals and that can eliminate the dualisms that categorize women differently from their male colleagues. Notes concerning seven suggested readings are appended. (JDD)

This working paper is one in a series of policy statements on the relationship between liberal education and careers in business. In this presentation, the president of the University of Chicago suggests that the importance of understanding education, particularly liberal arts education, as a complex subject is critical in understanding the relationship between liberal learning and private enterprise. Education, corporations, and management have a complex relationship that must be understood in order to put liberal arts in the proper perspective for the future. All people have ideas and theories about education, but little emphasis is placed on specific and complex ways of looking at changes in institutions of higher education and their relationship to other institutions. When considering education, it is necessary to bring in other topics, since thinking about education is in many ways like thinking about the future, present, and past. Ideas about what constitutes a good education have changed over the years; a liberal arts education was championed by the Renaissance humanists, who opposed the "sterile academicians" of their time. Liberal arts must be shown to be currently relevant in ways that differ from the merely technical or professional training. A fuller, more detailed sense of context for developing and teaching liberal arts is necessary. (SM)

An effective method for assessing responsiveness to adult undergraduates in institutions serving primarily traditional-age students is described. Information from previous literature was applied to the University of Massachusetts at Amherst in order to gather quantitative data on usage of certain practices plus a sense of campus receptivity to, and student satisfaction with, those effective practices. "Postsecondary Education Institutions and the Adult Learner: A Self-Study Assessment and Planning Guide" was used to develop the organizing framework, theoretical base, and an item pool for the survey instruments. Data were manipulated in order to, among other things: rank practices according to the extent of proponence and usage; rank widely applicable support service practices, and characterize the institution as a whole. The data indicate the university is potentially responsive to adult undergraduates, but there is not a widely shared attitude that adult undergraduates are legitimate and growing. Recommendations include that: the university direct additional and public support to an academic advisors council, an office of adult learning services be established, and a task force assess the need for after 4:00 P.M. course offerings, determine older student enrollment rates in independent studies, and improve the articulation of continuing education modes of instruction. Contains 32 references. (SM)

"Proceedings of International Conference on Social and Education Sciences" includes full papers presented at the International Conference on Social and Education Sciences (IConSES) which took place at the Holiday Inn & Suites Denver Tech Center-Centennial on October 7-10, 2019 in Denver, CO, USA. The aim of the conference is to offer opportunities to share your ideas, to discuss theoretical and practical issues and to connect with the leaders in the fields of education and social sciences. The conference is organized annually by the International Society for Technology, Education, and Science (ISTES) and supported by Iowa State University, Indiana University, and University of Northern Colorado. The IConSES invites submissions which address the theory, research or applications in all disciplines of education and social sciences. The IConSES is organized for: faculty members in all disciplines of education and social sciences graduate students, K-12 administrators, teachers, principals and all interested in education and social sciences After peer-reviewing process, all full papers are published in the Conference Proceedings. [These proceedings were published by the International Society for Technology, Education and Science (ISTES).]

There is increasing pressure to make pathways to higher education more accessible to students from a wide range of backgrounds, including those from lower socioeconomic circumstances. Often, those students gain vocational qualifications but those qualifications do not always support them fully to articulate to higher education. This is, at least in part, due to the gap in mathematics knowledge between what is taught in the vocational qualifications and what is required for entry into the related higher education courses. This paper reports on a project that took a cross-sector approach to improving pathways from vocational to higher education that focus on mathematical skill development. The project developed pathways for four disciplines, engineering, education, health science and business. This paper examines the challenges faced when working across those sectors to develop resources and establish pathways for students to articulate from vocational qualifications into related higher education courses. Of particular interest are the outcomes for the education pathway. A preliminary evaluation of the development of the education pathway resources is presented. The study utilises an exploratory research paradigm to explore the issues of working across sectors through content analysis of reports and surveys of stakeholders in the project.

Students begin their college studies with a set of epistemological beliefs about what they think knowledge is and how they think it is learned; for most students, the experience of college alters these beliefs in fundamental, transformative ways. This study explores the relation between epistemological beliefs, motivation, and cognition in two differing instructional contexts within the same mathematics course. The subjects included 438 first-semester calculus students at a large Midwestern research university. Students were enrolled in either experimental (New Wave) calculus sections or traditional calculus sections. Results indicated that intrinsic motivation and self-efficacy were correlated with sophistication of beliefs, though this was not true within the New Wave sections. This result suggests that students with more sophisticated beliefs, as measured by a strategies for learning questionnaire, are those students who reported that they are mastery-oriented and think that they are capable of doing well in mathematics. Intrinsically motivated students reported relative disagreement with the view of math as an isolated activity; the findings could be used to argue for the importance of group activities within mathematics. The results also provide some evidence for correlations between epistemological beliefs about mathematics and type of instruction. (RJM)

The Third International Conference on Data Mining (EDM 2010) was held in Pittsburgh, PA, USA. It follows the second conference at the University of Cordoba, Spain, on July 1-3, 2009 and the first edition of the conference held in Montreal in 2008, and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. EDM 2011 will be held in Eindhoven, Netherlands. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for analyzing the data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) Effort-based Tutoring: An Empirical Approach to Intelligent Tutoring (Ivon Arroyo, Hasmik Mehranian and Beverly P. Woolf); (2) An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools (Ryan S.J.d. Baker and Sujith M. Gowda); (3) On the Faithfulness of Simulated Student Performance Data (Michel C. Desmarais and Ildiko Pelczer); (4) Mining Bodily Patterns of Affective Experience during Learning (Sidney D'Mello and Art Graesser); (5) Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? (Mingyu Feng and Neil Heffernan); (6) Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System (Jon M. Fincham, John R. Anderson, Shawn Betts and Jennifer Ferris); (7) Using multiple Dirichlet distributions to improve parameter plausibility (Yue Gong, Joseph E. Beck and Neil T. Heffernan); (8) Examining Learner Control in a Structured Inquiry Cycle Using Process Mining (Larry Howard, Julie Johnson and Carin Neitzel); (9) Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models (Hogyeong Jeong, Gautam Biswas, Julie Johnson and Larry Howard); (10) Data Mining for Generating Hints in a Python Tutor (Anna Katrina Dominguez, Kalina Yacef and James R. Curran); (11) Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity (Blair Lehman, Whitney Cade and Andrew Olney); (12) Sentiment Analysis in Student Experiences of Learning (Sunghwan Mac Kim and Rafael A. Calvo); (13) Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (14) A Data Model to Ease Analysis and Mining of Educational Data (Andre Kruger, Agathe Merceron and Benjamin Wolf); (15) Identifying Students' Inquiry Planning Using Machine Learning (Orlando Montalvo, Ryan S.J.d. Baker, Michael A. Sao Pedro, Adam Nakama and Janice D. Gobert); (16) Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers (Rebecca Nugent, Nema Dean and Elizabeth Ayers); (17) Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm (Zachary Pardos and Neil Heffernan); (18) Mining Rare Association Rules from e-Learning Data (Cristobal Romero, Jose Raul Romero, Jose Maria Luna and Sebastian Ventura); (19) Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns (Michael Sao Pedro, Ryan S.J.d. Baker, Orlando Montalvo, Adam Nakama and Janice D. Gobert); (20) Identifying High-Level Student Behavior Using Sequence-based Motif Discovery (David H. Shanabrook, David G. Cooper, Beverly Park Woolf and Ivon Arroyo); (21) Unsupervised Discovery of Student Strategies (Benjamin Shih, Kenneth R. Koedinger and Richard Scheines); (22) Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data (Wenting Xiong, Diane Litman and Christian Schunn); (23) Using Numeric Optimization To Refine Semantic User Model Integration Of Adaptive Educational Systems (Michael Yudelson, Peter Brusilovsky, Antonija Mitrovic and Moffat Mathews); (24) An Annotations Approach to Peer Tutoring (John Champaign and Robin Cohen); (25) Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning (Mohammad Hassan Falakmasir and Jafar Habibi); (26) Mining Students' Interaction Data from a System that Support Learning by Reflection (Rajibussalim); (27) Process Mining to Support Students' Collaborative Writing (Vilaythong Southavilay, Kalina Yacef and Rafael A. Callvo); (28) Automatic Rating of User-Generated Math Solutions (Turadg Aleahmad, Vincent Aleven and Robert Kraut); (29) Tracking Students' Inquiry Paths through Student Transition Analysis (Matt Bachmann, Janice Gobert and Joseph Beck); (30) DISCUSS: Enabling Detailed Characterization of Tutorial Interactions Through Dialogue Annotation (Lee Becker, Wayne H. Ward and Sarel vanVuuren); (31) Data Mining of both Right and Wrong Answers from a Mathematics and a Science M/C Test given Collectively to 11,228 Students from India [1] in years 4, 6 and 8 (James Bernauer and Jay Powell); (32) Mining information from tutor data to improve pedagogical content knowledge (Suchismita Srinivas, Muntaquim Bagadia and Anupriya Gupta); (33) Clustering Student Learning Activity Data (Haiyun Bian); (34) Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments (Nabila Bousbia, Jean-Marc Labat, Amar Balla and Issam Rebai); (35) Using Topic Models to Bridge Coding Schemes of Differing Granularity (Whitney L. Cade and Andrew Olney); (36) A Distillation Approach to Refining Learning Objects (John Champaign and Robin Cohen); (37) A Preliminary Investigation of Hierarchical Hidden Markov Models for Tutorial Planning (Kristy Elizabeth Boyer, Robert Phillips, Eun Young Ha, Michael D. Wallis, Mladen A. Vouk, and James C. Lester); (38) Higher Contributions Correlate with Higher Learning Gains (Carol Forsyth, Heather Butler, Arthur C. Graesser, Diane Halpern); (39) Pinpointing Learning Moments; A finer grain P(J) model (Adam Goldstein, Ryan S.J.d. Baker and Neil T. Heffernan); (40) Predicting Task Completion from Rich but Scarce Data (Jose P. Gonzalez-Brenes and Jack Mostow); (41) Hierarchical Structures of Content Items in LMS (Sharon Hardof-Jaffe, Arnon Hershkovitz, Ronit Azran and Rafi Nachmias); (42) Is Students' Activity in LMS Persistent? (Arnon Hershkovitz and Rafi Nachmias); (43) EDM Visualization Tool: Watching Students Learn (Matthew M. Johnson and Tiffany Barnes); (44) Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks (Nabila Khodeir, Nayer Wanas, Nevin Darwish and Nadia Hegazy); (45) Using LiMS (the Learner Interaction Monitoring System) to Track Online Learner Engagement and Evaluate Course Design (Leah P. Macfadyen and Peter Sorenson); (46) Observing Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (47) When Data Exploration and Data Mining meet while Analysing Usage Data of a Course (Andre Kruger, Agathe Merceron and Benjamin Wolf); (48) AutoJoin: Generalizing an Example into an EDM query (Jack Mostow and Bao Hong (Lucas) Tan); (49) Conceptualizing Procedural Knowledge Targeted at Students with Different Skill Levels (Martin Mozina, Matej Guid, Aleksander Sadikov, Vida Groznik, Jana Krivec, and Ivan Bratko); (50) Data Reduction Methods Applied to Understanding Complex Learning Hypotheses (Philip I. Pavlik Jr.); (51) Analysis of a causal modeling approach: a case study with an educational intervention (Dovan Rai and Joseph E. Beck); (52) Peer Production of Online Learning Resources: A Social Network Analysis (Beijie Xu and Mimi M. Recker); (53) Class Association Rules Mining from Students' Test Data (Cristobal Romero, Sebastian Ventura, Ekaterina Vasilyeva and Mykola Pechenizkiy); (54) Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for Epistemic Games (Andre A. Rupp, Shauna J. Sweet and Younyoung Choi); (55) Multiple Test Forms Construction based on Bees Algorithm (Pokpong Songmuang and Maomi Ueno); (56) Can Order of Access to Learning Resources Predict Success? (Hema Soundranayagam and Kalina Yacef); (57) A Data Driven Approach to the Discovery of Better Cognitive Models (Kenneth R. Koedinger and John C. Stamper); (58) Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems (John C. Stamper, Tiffany Barnes and Marvin Croy); (59) A Review of Student Churn in the Light of Theories on Business Relationships (Jaan Ubi and Innar Liiv); (60) Towards EDM Framework for Personalization of Information Services in RPM Systems (Ekaterina Vasilyeva, Mykola Pechenizkiy, Aleksandra Tesanovic, Evgeny Knutov, Sicco Verwer and Paul De Bra); (61) A Case Study: Data Mining Applied to Student Enrollment (Cesar Vialardi, Jorge Chue, Alfredo Barrientos, Daniel Victoria, Jhonny Estrella, Juan Pablo Peche and Alvaro Ortigosa); (62) Representing Student Performance with Partial Credit (Yutao Wang, Neil T. Heffernan and Joseph E. Beck); (63) Where in the World? Demographic Patterns in Access Data (Mimi M. Recker, Beijie Xu, Sherry Hsi, and Christine Garrard); and (64) Pundit: Intelligent Recommender of Courses (Ankit Ranka, Faisal Anwar, Hui Soo Chae). Individual papers contain tables, figures, footnotes and references