creased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes—although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.

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Active learning increases student performance in science, engineering, and mathematics

We describe a novel randomized method, the method of color-coding for finding simple paths and cycles of a specified length k, and other small subgraphs, within a given graph G = (V,E). The randomized algorithms obtained using this method can be derandomized using families of perfect hash functions. Using the color-coding method we obtain, in particular, the following new results: • For every fixed k, if a graph G = (V,E) contains a simple cycle of size exactly k, then such a cycle can be found in either O(V ) expected time or O(V ω log V ) worst-case time, where ω < 2.376 is the exponent of matrix multiplication. (Here and in what follows we use V and E instead of |V | and |E| whenever no confusion may arise.) • For every fixed k, if a planar graph G = (V,E) contains a simple cycle of size exactly k, then such a cycle can be found in either O(V ) expected time or O(V log V ) worst-case time. The same algorithm applies, in fact, not only to planar graphs, but to any minor closed family of graphs which is not the family of all graphs. • If a graph G = (V,E) contains a subgraph isomorphic to a bounded tree-width graph H = (VH , EH) where |VH | = O(log V ), then such a copy of H can be found in polynomial time. This was not previously known even if H were just a path of length O(log V ). These results improve upon previous results of many authors. The third result resolves in the affirmative a conjecture of Papadimitriou and Yannakakis that the LOG PATH problem is in P. We can show that it is even in NC.

Color-coding

Dynamic memory allocation has been a fundamental part of most computer systems since roughly 1960, and memory allocation is widely considered to be either a solved problem or an insoluble one. In this survey, we describe a variety of memory allocator designs and point out issues relevant to their design and evaluation. We then chronologically survey most of the literature on allocators between 1961 and 1995. (Scores of papers are discussed, in varying detail, and over 150 references are given.)

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Dynamic Storage Allocation: A Survey and Critical Review

We present an assortment of methods for finding and counting simple cycles of a given length in directed and undirected graphs. Most of the bounds obtained depend solely on the number of edges in the graph in question, and not on the number of vertices. The bounds obtained improve upon various previously known results.

/pdf/finding-and-counting-given-length-cycles-fm5ncvaifq.pdf

Finding and counting given length cycles

Given a set of entities, Cluster Analysis aims at finding subsets, called clusters, which are homogeneous and/or well separated. As many types of clustering and criteria for homogeneity or separation are of interest, this is a vast field. A survey is given from a mathematical programming viewpoint. Steps of a clustering study, types of clustering and criteria are discussed. Then algorithms for hierarchical, partitioning, sequential, and additive clustering are studied. Emphasis is on solution methods, i.e., dynamic programming, graph theoretical algorithms, branch-and-bound, cutting planes, column generation and heuristics.

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Cluster analysis and mathematical programming

Smallest-last vertex ordering and prlonty search are utdlzed to show for any graph G = (IT, E) that the set of all connected subgraphs maxunal with respect to their minimum degree can be determined in O(I EI + I VI) time and 21El + O(I VI) space It is further noted that the smallest-last graph coloring algonthrn can be unplemented in O(I E I + I V[) tune, and particularly effective aspects of the resulting coloring are discussed.

Smallest-last ordering and clustering and graph coloring algorithms

Cooperative learning is a well-known instructional technique that has been applied with a wide variety of subject matter and a broad spectrum of populations. This article briefly reviews the principles of cooperative learning, and describes how these principles were incorporated into a comprehensive set of cooperative learning activities for a CS1 course. In each activity, specific roles are assigned to group members in order to highlight important concepts and to enhance the overall functioning of the group. The group processing is followed by a whole-class debriefing led by the instructor, which works in tandem with the group activity to help students improve their understanding of the material. The effectiveness of these cooperative learning activities was assessed in a series of educational research studies which spanned three academic years and included two different instructors. The results of these studies show statistically significant benefits from the cooperative learning approach, both overall and for a broad range of subgroups of students. The article concludes with suggestions for faculty members who may want to use these cooperative learning activities in the classroom, or to develop their own activities along similar lines.

Cooperative learning instructional methods for CS1: Design, implementation, and evaluation

A set of cooperative learning exercises were designed for use in a Java-based CS1 course. The exercises used specific roles to focus students' attention on key concepts of the Java language, and on key mental processes of programming and problem solving. A controlled experiment was conducted to evaluate the effectiveness of this approach. The results show that the cooperative learning experience had a significant positive effect on student performance, and may have been of special benefit to women and minority students.

Cooperative learning techniques in CS1: design and experimental evaluation

An experiment was conducted to study the effectiveness of the cooperative learning approach to teaching CS1. The cooperative learning exercises, which used specific roles to focus students' attention on key concepts, were designed so they could be used in a variety of educational settings. Experimental results show that the benefits of cooperative learning clearly outweighed any possible losses due to reduced lecture time. These benefits were enjoyed by both male and female students, and by students from a variety of majors. Majority and minority students performed at approximately the same overall level when using the cooperative learning approach. There are indications that the educational benefits continue when students who had a cooperative learning experience in CS1 go on to CS2.

/pdf/an-experimental-study-of-cooperative-learning-in-cs1-536xhlue35.pdf

An experimental study of cooperative learning in cs1

1 Background Introduction System Software and Machine Structure The Simplified Instructional Computer (SIC) Traditional (CISC) Architectures RISC Architectures 2 Assemblers Basic Assembler Functions Machine-Dependent Assembler Features Machine-Independent Assembler Features Assembler Design Options Implementation Examples 3 Loaders and Linkers Basic Loader Functions Machine-Dependent Loader Features Machine-Independent Loader Features Loader Design Options Implementation Examples 4 Macro Processors Basic Macro Processor Functions Machine-Independent Macro Processor Features Macro Processor Design Options Implementation Examples 5 Compilers Basic Compiler Functions Machine-Dependent Compiler Features Machine-Independent Compiler Features Compiler Design Options Implementation Examples 6 Operating Systems Basic Operating System Functions Machine-Dependent Operating System Features Machine-Independent Operating System Features Operating System Design Options Implementation Examples 7 Other System Software Database Management Systems (DBMS) Text Editors Interactive Debugging Systems 8 Software Engineering Issues Introduction to Software Engineering Concepts System Specifications Procedural Design Object-Oriented Design System Testing Strategies 0201423006T04062001

Leland L. Beck

Papers

Smallest-last ordering and clustering and graph coloring algorithms

Cooperative learning instructional methods for CS1: Design, implementation, and evaluation

Cooperative learning techniques in CS1: design and experimental evaluation

An experimental study of cooperative learning in cs1

System Software: An Introduction to Systems Programming