Flip your classroom: Reach every student in every class every day

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The New Meaning Of Educational Change

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

[font face="Verdana, Helvetica" size="2"An ideal companion to many introductory computer science courses, as well as an excellent primer, Ruby: The Programming Language offers an accessible introduction to this easy to learn, open-source language. This overview describes the fundamentals of the object-oriented Ruby language and discusses how students can quickly construct complete web applications with ease. Ruby: The Programming Language will excite students on the capabilities of computer programming and inspire them to delve deeper into the computer science discipline.

Ruby: The Programming Language

Fifty Modern Thinkers on Education: From Piaget to the Present

In this paper, we provide evidence that analysis of student interaction with web-based pedagogical tools in a large partly-flipped CS1 class can be used for early detection of at-risk students. We used student interaction data to estimate five learning style features: engagement, learning speed, confidence, drive, and persistence. We found a positive correlation between final course grades and two of the features of student learning style: engagement and learning speed. Drive and persistence were not good predictors of student success but did provide lower bounds on overall course performance. Confidence was a poor predictor of overall course performance. This kind of analysis provided a more timely and nuanced view of student learning styles than can be obtained from traditional summative assessments and this data could allow for early detection of at-risk students in an introductory programming course.

Early detection of at-risk students in CS1 using teachback/spinoza

Flipping a classroom involves a more interactive class model where students and instructors spend the majority of the class time on various interactive activities in engagement with class materials. Often, this pedagogy style involves taking advantage of the new interactive technologies. In this work, we describe an experiment in an introduction to programming class (CS1) in which we compared the outcomes of offering the same interactive classroom with and without computers. The first approach required students to bring computers to class to engage with the class and materials individually and in groups using the computer-enabled tools. The other approach was to ban computers and require students to interact in person and engage with materials using pen and paper. In both approaches the students’ attempts are shared with the class and discussed, and in general we attempted to maintain the same class models. We found that the use of computers alone had no statistically significant effect on the students’ learning outcomes, enjoyment of the material, self-assessment of their understanding, use of teaching assistant resources, or self-estimate of how many hours they invested outside of the classroom. We did find that a statistically significant number of students preferred in-class engagements and interactions using computers. We also found that the instructor had much more useful and detailed information about individual student’s interaction in class when computers were used. We conclude that, although many instructors are wary of requiring computer use in large classes, there is evidence that students prefer it, it does not negatively affect learning outcomes, and with appropriate tools and pedagogy, it gives the instructor a much deeper and more nuanced view of student performance in the class.

A Flipped Classroom with and Without Computers

This paper proposes a simple method to add optional live-streaming to a highly interactive class in a way that reduces the number of students physically present in the classroom and doesn't hurt student performance. The effect of allowing live-streaming was analyzed by running an experiment where partway through the semester students in a flipped advanced level Computer Graphics class were given the option to attend the class remotely using a live-stream of the class with required use of a multi-featured audience response system (TeachBack). The results show that allowing remote attendance with TeachBack decreased the number of students attending physically from 93% to 75% of registered students and had no effect on unexcused absences. Students believed that their remote attendance was just as effective for their learning as attending classes face-to-face and analysis of the data confirmed their beliefs; they did however generally prefer being physically present in class. Our results suggest that appropriate use of live-streaming coupled with an Audience Response System can reduce some of the demand for large lecture halls by allowing a self-selected subset of students to attend some or all of the classes remotely. A large percentage of the students in the class felt that a live-streaming option should be added to most classes and this paper demonstrates that this goal is feasible and pedagogically justifiable.

Fully integrating remote students into a traditional classroom using live-streaming and TeachBack

In this paper we present our initial work on an application, The Affective Tutor, envisioned to help instructors keep all students in a large lecture class engaged and actively learning material related to the course. We have found, by asking for feedback after every class, that in a large class of over 50 students, a significant number of students will feel the class went too slow and another group will feel the class went too fast. One of the most challenging aspects of teaching a large class is to keep the entire class engaged in the course material. The Affective Tutor helps instructors to determine in real-time how students feel about the pace of the class. The tool also helps instructors to pin-point confusing topics, and to tackle the problem of keeping students engaged by providing a monitored back-channel that allows the bored students to help the confused students get back on track and into an engaged mode.

The affective tutor

It is a difficult task to generate optimal walking gaits for mobile legged robots. Generating and coordinating an optimal gait involves continually repeating a series of actions in order to create a sustained movement. In this work, we present the use of a Cyclic Genetic Algorithm (CGA) to learn near optimal gaits for an actual quadruped servo-robot with three degrees of movement per leg. This robot was used to create a simulation model of the movement and states of the robot which included the robot's unique features and capabilities. The CGA used this model to learn gaits that were optimized for this particular robot. Tests done in simulation show the success of the CGA in evolving gait control programs and tests on robot show that these control programs produce reasonable gaits.

/pdf/using-cyclic-genetic-algorithms-to-learn-gaits-for-an-actual-2k8chlh76j.pdf

William T. Tarimo

Papers

Early detection of at-risk students in CS1 using teachback/spinoza

A Flipped Classroom with and Without Computers

Fully integrating remote students into a traditional classroom using live-streaming and TeachBack

The affective tutor

Using Cyclic Genetic Algorithms to learn gaits for an actual quadruped robot