In this workshop, we will describe the numerous processes and tasks involved in successfully hosting a high school programming contest. We will describe the mechanics of running the contest using PC^2, within the logistics of a college campus environment. We will talk about the logistics necessary to support the number of high schools and teams (currently we involve 15+ high schools and over 60 4-person teams). Finally, we will describe our local chapter of CSTA, which strengthens bonds and connections between ourselves and the school's coaches and advisors.

An implicit association test is a human psychological test used to measure subconscious associations. While widely recognized by psychologists as an effective tool in measuring attitudes and biases, the validity of the results can be compromised if a subject does not follow the instructions or attempts to manipulate the outcome. Compared to previous work, we collect training data using a more generalized methodology. We train a variety of different classifiers to identify a participant's first attempt versus a second possibly compromised attempt. To compromise the second attempt, participants are shown their score and are instructed to change it using one of five randomly selected deception methods. Compared to previous work, our methodology demonstrates a more robust and practical framework for accurately identifying a wide variety of deception techniques applicable to the IAT.

While some research has suggested that video lectures are just as effective as in-person lectures to convey basic information to students, not everyone agrees that the flipped classroom model is an effective way of educating students. This research explores traditional, semi-flipped and fully-flipped classroom models by comparing three sections of an Introduction to Programming (Java) course that were taught at the same institution in Spring 2015 by three different instructors using three different paradigms. The data and observations collected suggests that incorporating in-class activities improves student satisfaction but a semi-flipped classroom, including in-class activities, some outsideclass lecture videos, and some in-class lectures, may generally provide the best overall experience for the students. However, while students may be more satisfied and get more programming practice in a flipped paradigm, overall student performance did not appear to be greatly impacted.

Creating video lectures and in-class activities for flipped courses requires considerable effort. It is natural to secure such content behind closed learning management systems, so the hard work of the author is not used at other institutions. However, 4-year institutions that seek prepared transfer students can benefit from sharing material in a targeted way. This paper describes a pilot project where flipped course material from a web design course at a 4-year college was shared with a similar course at a nearby 2-year college. This course is frequently transferred to the 4-year college and prepares students for upper-level courses. Sharing course material in this way can directly improve the curriculum alignment of 2-year and 4-year programs to create better pipelines that benefit both institutions. This model has potential to increase transfer rates while directly preparing students for advanced courses in 4-year programs. In this paper, we describe the methodology and benefits, share observations and survey results, and outline future plans to expand this project.

Given the popularity of YouTube and other video dissemination websites, it is reasonable to assume that the digital natives of today might prefer video-based instruction, especially for a computer lab activity in an Information Systems course. Consider the specificity of a video that shows students exactly what to click versus the potential ambiguity of text instructions. At first look, video appears to offer a significantly superior instruction format compared to text, especially for the digital native generation. However, after two years of experimenting with video instructions, we no longer make this assumption. We present an experiment that examines the differences between online video and online text instructions with respect to concept learning, task completion time, retention, and student impression. Our results indicate that video and text perform similarly, which is surprising since we are investigating a type of task and an audience that appear, on the surface, to be well-suited for video instructions. Our experiments are supported by an innovative web-based lab delivery system that provides a framework for evaluating different forms of instruction and user interface designs by collecting students' responses, impressions of the experience, and timing data.

Market Basket Analysis is an important topic to cover in a Management Information Systems course. Rather than teach only the concept, our philosophy is to teach the topic using hands-on activities where students perform an analysis on a small but non-trivial data set. Our approach does not require knowledge of SQL, programming, or special software. Students use simple Microsoft Access functionality to find frequent itemsets and association rules. We believe this approach is a rigorous and engaging way to teach Market Basket Analysis that is most appropriate for an introductory course. Our follow-on Business Database course revisits the topic in a more technical manner where students write SQL queries. Thus, students are introduced to various SQL features through a fundamentally important topic that they have already seen in the prerequisite course. This paper describes the cognitive support structures used to introduce Market Basket Analysis, the details of how the activities are performed without SQL, and how we reinforce the topic with SQL in our Business Database course.

While online systems are an essential component of distance learning, they can also play a critical role in improving the delivery of activities in a traditional laboratory setting. The quality and effectiveness of online course delivery is often compared to equivalent face-to-face alternatives. In our approach, we have harnessed what we feel to be the best of both delivery methods. We have developed a web-based system with some novel characteristics for use in a face-to-face laboratorybased Management Information Systems course. To assess its impact, we surveyed 110 students where approximately half completed the lab using our web-based system while the other half completed a paper-based equivalent. The promising results have encouraged us to develop further experiments to measure student performance as well as the impact of video versus text in presenting activity instructions.

In this paper we study key differences between video-based and text-based instructions by developing and testing an interactive website for delivering lab material in our Management Information Systems course. In a face-to-face lab setting, we tracked the performance and surveyed the impressions of 80 students where approximately half received video instructions while the other half received text instructions. The results indicate no statistically significant difference in students' correctness in answering lab questions, but slight differences in completion time and impression. We discuss our results and suggest ways to use video instruction effectively based on our results and observations.

We propose a new paradigm for building a Management Information Systems course that focuses on laboratory activities developed collaboratively using Computer-Mediated Communication and Collaboration tools. A highlight of our paradigm is the “practice what you preach” concept where the computer communication tools and collaboration concepts covered in lecture are actually used by both instructors and students to collaborate on labs and projects. Using computer-mediated communication to build a course facilitates stigmergic collaboration where course material is truly the evolving creation of a large group of instructors. Using these communication tools gives instructors additional experience and perspective in teaching computer-mediated communication concepts. Requiring students to use the same communication tools in a course setting gives them collaborative and technical skills to solve problems and communicate solutions to others, while also seamlessly integrating them into the course development process.

Management Information Systems is a common course requirement for business programs, but it is often taught as a service course within a Computer Science department. When taught as an entirely lecture-based course, Management Information Systems can seem unrelated to fundamental Computer Science topics. When computer application proficiency is the focus, Management Information Systems can be an unpopular course to teach. However, some departments may be missing out on an excellent opportunity to attract students to the field of Computer Science. By focusing on innovative labs that combine business applications with Computer Science-based solutions, it is possible to teach practical computer application skills, traditional material in Management Information Systems, and both fundamental and emerging Computer Science topics.

In this paper, we describe the supervised learning approach to optimization problems in the spirit of the PAC learning model. By this approach, we discover domain-specific algorithms by learning from an oracle, which is also an optimization algorithm for the problem in question. We describe examples of learning backtracking-based algorithms and algorithms that implement the dynamic programming paradigm.

This paper addresses the problem of detecting masquerading, a security attack in which an intruder assumes the identity of a legitimate user. Many approaches based on Hidden Markov Models and various forms of Finite State Automata have been proposed to solve this problem. The novelty of our approach results from the application of techniques used in bioinformatics for a pair-wise sequence alignment to compare the monitored session with past user behavior. Our algorithm uses a semi-global alignment and a unique scoring system to measure similarity between a sequence of commands produced by a potential intruder and the user signature, which is a sequence of commands collected from a legitimate user. We tested this algorithm on the standard intrusion data collection set. As discussed in the paper, the results of the test showed that the described algorithm yields a promising combination of intrusion detection rate and false positive rate, when compared to published intrusion detection algorithms.

We present an experimental study of a learning algorithm for the longest common subsequence problem, LCS . Given an arbitrary input domain, the algorithm learns an LCS -procedure tailored to that domain. The learning is done with the help of an oracle, which can be any LCS -algorithm. After solving a limited number of training inputs using an oracle, the learning algorithm outputs a new LCS -procedure.Our experiments demonstrate that, by allowing a slight loss of optimality, learning yields a procedure which is significantly faster than the oracle. The oracle used for the experiments is the np -procedure by Wu et al. , a modification of Myers' classical LCS -algorithm. We show how to scale up the results of learning on small inputs to inputs of arbitrary lengths. For the domain of two random 2-symbol inputs of length n , learning yields a program with 0.999 expected accuracy, which runs in O ( n 1.41 )-time, in contrast with O ( n 2 /log n ) running time of the fastest theoretical algorithm that produces optimal solutions. For the domain of random 2-symbol inputs of length 100,000, the program runs 10.5 times faster than the np -procedure, producing 0.999- accurate outputs. The scaled version of the evolved algorithm applied to random inputs of length 1 million runs approximately 30 times faster than the np -procedure while constructing 0.999- accurate solutions. We apply the evolved algorithm to DNA sequences of various lengths by training on random 4-symbol sequences of up to length 10,000. The evolved algorithm, scaled up to the lengths of up to 1.8 million, produces solutions with the 0.998-accuracy in a fraction of the time used by the np .

We describe a supervised learning approach to resolve difficulties in finding biologically significant local alignments. It was noticed that the O(n(2)) algorithm by Smith-Waterman, the prevalent tool for computing local sequence alignment, often outputs long, meaningless alignments while ignoring shorter, biologically significant ones. Arslan et. al. proposed an O(n(2) log n) algorithm which outputs a normalized local alignment that maximizes the degree of similarity rather than the total similarity score. Given a properly selected normalization parameter, the algorithm can discover significant alignments that would be missed by the Smith-Waterman algorithm. Unfortunately, determining a proper normalization parameter requires repeated executions with different parameter values and expert feedback to determine the usefulness of the alignments. We propose a learning approach that uses existing biologically significant alignments to learn parameters for intelligently processing sub-optimal Smith-Waterman alignments. Our algorithm runs in O(n(2)) time and can discover biologically significant alignments without requiring expert feedback to produce meaningful results.

We investigate, through numerical experiments, the asymptotic behavior of the length Hd(n) of a maximal chain (longest totally ordered subset) of a set of n points drawn from a uniform distribution on the d-dimensional unit cube V D = [0, 1]d. For d ≥ 2, it is known that cd(n) = Hd(n)/n1/d converges in probability to a constant Cd < e, with Iim d→∞ Cd = e. For d = 2, the problem has been extensively studied, and it is known that C2 = 2; Cd is not currently known for any d ≥ 3. Straightforward Monte Carlo simulations to obtain Cd have already been proposed, and shown to be beyond the scope of current computational resources. In this paper, we present a computational approach which yields feasible experiments that lead to estimates for Cd. We prove that Hd(n) can be estimated by considering only those chains close to the diagonal of the cube. A new conjecture regarding the asymptotic behavior of cd(n) leads to even more efficient experiments. We present experimental support for our conjecture, and the new estimates of Cd obtained from our experiments, for d ∈ {3,4,S,6}.

We develop computationally feasible algorithms to numerically investigate the asymptotic behavior of the length H d (n) of a maximal chain (longest totally ordered subset) of a set of n points drawn from a uniform distribution on the d-dimensional unit cube Vd = [0, 1]d. For d ≥ 2, it is known that c d (n) = H d (n)/n 1/d converges in probability to a constant c d < e, with limd→∞ c d = e. For d = 2, the problem has been extensively studied, and, it is known that c2 = 2. Monte Carlo simulations coupled with the standard dynamic programming algorithm for obtaining the length of a maximal chain do not yield computationally feasible experiments. We show that H d (n) can be estimated by considering only the chains that are close to the diagonal of the cube and develop efficient algorithms for obtaining the maximal chain in this region of the cube. We use the improved algorithm together with a linearity conjecture regarding the asymptotic behavior of c d (n) to obtain even faster convergence to c d . We present experimental simulations to demonstrate our results and produce new estimates of c d for d ∈ {3,..., 6}.

Users of complex applications need advice, assistance, and feedback while they work. We are experimenting with “adjunct” user agents that are aware of the history of interaction surrounding the accomplishment of a task. This paper describes an architectural framework for constructing these agents. Using this framework, we have implemented a critiquing system that can give task-oriented critiques to trainees while they use operating system tools and software applications. Our approach is generic, widely applicable, and works directly with off-the-shelf software packages.

This paper describes several aspects of our on-going research relating to technologies and techniques for collaborative learning. Our research is focused on the classroom-in-the-round, a CSCW enabled classroom that attempts to promote collaborative learning through novel room design, hardware, software and courseware. Encouraging preliminary feedback from courses we are teaching in the classroom is presented. An in-depth study conducted to evaluate the effectiveness of assigned roles on collaboration between pairs of users in a synchronous groupware simulation is then discussed. Statistically significant results show that assigned roles increase team interaction and improve group performance in subsequent unrelated group activities, when compared to a control group. The paper closes with some remarks on promising instructional techniques we are investigating for collaborative learning, including distributed grading, on- line presentation of team projects, and team-oriented presentation-style exams.