Order Of Examples Research Articles

The influence of the order and congruency of correct and erroneous worked examples on learning and (meta-)cognitive load.

Several studies highlight the importance of the order of different instructional methods when designing learning environments. Correct but also erroneous worked examples are frequently used methods to foster students' learning performance, especially in problem-solving. However, so far no study examined how the order of these example types affects learning. While the expertise reversal effect would suggest presenting correct examples first, the productive failure approach hypothesizes the reversed order to be learning-facilitating. In addition, congruency of subsequent exemplified problems was tested as a moderator of the effect of order on learning. For example, with arithmetic tasks, congruent problems target exactly the same calculation while incongruent problems refer to different calculations. Following cascade theory, a model of cognitive skill acquisition, presenting correct examples first should be more effective when the subsequent exemplified problems are different. To test the (conflicting) hypotheses, 83 university students were assigned to one of the four conditions in a 2 (correct vs. erroneous example first) × 2 (same vs. different exemplified problems) between-subject design. Learners navigated through a slideshow on the topic of Vedic mathematics consisting of explicit instruction, worked examples differing in terms of the experimental condition, and transfer problems. Although no main or interaction effects were found regarding students' learning performance, mediational analysis offered support for the expertise reversal effect, as it indicated that there is a significant indirect effect of order via mental load on learning. Presenting correct examples first and erroneous examples second resulted in a lower mental load, which in turn was associated with better learning performance. In contrast, presenting erroneous examples first and correct examples second resulted in a more accurate self-assessment of learning performance. These findings offer first insights into the question of how the presentation order of different example types impacts learning and provide practical recommendations for the design of educational media. Results are discussed in light of the ongoing debate regarding the question if less guided instructional methods should precede or succeed more guided methods.

A systematic review of interleaving as a concept learning strategy

AbstractA systematic review was conducted into the effect of interleaving the order of examples of concepts in terms of both memory of items and transfer to new items. This concept has important implications for how and when teachers present examples in the classroom. A total of 26 studies met the inclusion criteria; a subset of 17 studies (with 32 constituent datasets) formed the basis of a meta‐analysis, and the remainder were analysed within a narrative review. Memory (as tested by presenting studied items from a learned category) showed an interleaving benefit with effect sizes (Hedges’ g) of up to 0.65, and transfer (as tested by presenting novel items from a learned category) a benefit with effect sizes of up to 0.66. Interleaving was found to be of greatest use when differences between items are subtle, and the benefit extended to both art‐ and science‐based items, with implication for practitioner decisions over how and when to apply the technique. It also extended to delayed tests. The review revealed that the literature is dominated by laboratory studies of university undergraduates, and the need for future school‐based research using authentic classroom tasks is outlined.

Effects of problem\u2013example and example\u2013problem pairs on gifted and nongifted primary school students\u2019 learning

Example-based learning (i.e., studying examples to learn a problem–solution procedure, often alternated with solving practice problems) leads to better learning outcomes than solving practice problems only, and video examples are increasingly being used in online and blended learning environments. Recent findings show that the presentation order of examples and problems affects learning: Example–problem pairs have been found to be more effective than problem–example pairs. We investigated a motivational explanation for this difference, which states that starting with a practice problem might be too difficult, causing learners to lose confidence and motivation to study. We investigated this by presenting gifted (n = 61) and nongifted primary school students (n = 65) with two problem–example or example–problem pairs. We hypothesized that gifted students, who generally report higher perceived competence and autonomy and higher need for cognition, would be less affected by the difficulty of starting with a problem. As expected, gifted students indeed reported higher motivation and confidence than nongifted students, and gifted students were more efficient learners. In contrast to our expectations, however, there was no difference between gifted and nongifted students in the effect of the different task sequences on test performance. Studying example–problem pairs was more efficient than studying problem–example pairs, both for gifted and nongifted students.

Controlled permutations for testing adaptive learning models

We study evaluation of supervised learning models that adapt to changing data distribution over time (concept drift). The standard testing procedure that simulates online arrival of data (test-then-train) may not be sufficient to generalize about the performance, since that single test concludes how well a model adapts to this fixed configuration of changes, while the ultimate goal is to assess the adaptation to changes that happen unexpectedly. We propose a methodology for obtaining datasets for multiple tests by permuting the order of the original data. A random permutation is not suitable, as it makes the data distribution uniform over time and destroys the adaptive learning task. Therefore, we propose three controlled permutation techniques that make it possible to acquire new datasets by introducing restricted variations in the order of examples. The control mechanisms with theoretical guarantees of preserving distributions ensure that the new sets represent close variations of the original learning task. Complementary tests on such sets allow to analyze sensitivity of the performance to variations in how changes happen and this way enrich the assessment of adaptive supervised learning models.

Decision trees for mining data streams

In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT. We have extended VFDT in three directions: the ability to deal with continuous data; the use of more powerful classification techniques at tree leaves, and the ability to detect and react to concept drift. VFDTc system can incorporate and classify new information online, with a single scan of the data, in time constant per example. The most relevant property of our system is the ability to obtain a performance similar to a standard decision tree algorithm even for medium size datasets. This is relevant due to the any-time property. We also extend VFDTc with the ability to deal with concept drift, by continuously monitoring differences between two class-distribution of the examples: the distribution when a node was built and the distribution in a time window of the most recent examples. We study the sensitivity of VFDTc with respect to drift, noise, the order of examples, and the initial parameters in different problems and demonstrate its utility in large and medium data sets.

Learning Best Concept Approximations from Examples

This paper addresses the problem of learning the best approximation of a concept from examples, when the concept cannot be expressed in the learner’s representation language. It presents a method that determines the version space of the best approximations and demonstrates that for any given approximation of the target concept there is a better approximation in this version space. The method does not depend on the order of examples and has an almost monotonic convergence. This method was developed for the Disciple learning agent that can be taught by a subject matter expert how to perform complex problem solving tasks.

The effects of order on learning classifications by example: Heuristics for finding the optimal order

The effects of the order of training examples on the performance of learning models is examined, and shown to be an important variable affecting the number of classification errors which occur. Instances are found where changing the order of examples can cause error rates to vary from 0% to 67%. This level of variation may cause problems in testing or comparing learning programs, where it may be useful to test performance using the best-possible order of training examples. Two heuristics for finding the optimal order of examples are proposed and tested. On 96 variations of two classification problems the heuristics found either optimal or next-to-optimal solutions.

The Effects of Recycling and Response Sensitivity on the Acquisition of Social Studies Concepts

Abstract Findings from two studies that examined the effect of presentation order of examples and nonexamples on students' acquisition of concepts are reported. The first (Study I) compared the effect of a response-sensitive presentation (presentation order determined by the students' response) to a response-insensitive presentation (order predetermined and presented in random order). In addition, the effects of recycling missed instances were examined. The sample consisted of 116 sixth grade students who were randomly assigned to one of two treatment groups or a control group. The concept of transfer propaganda was taught by two experienced teachers. Findings indicated that presentations that were response-sensitive and/or included the recycling of missed instances were no more effective than presentations that were insensitive or excluded recycling. The second (Study 2) was an attempt to replicate the findings concerning the recycling of missed instances in Study 1. In the second, 107 second grade stude...

The Effects of the Presentation Order of Examples and Nonexamples on First-Grade Students' Acquisition of Coordinate Concepts

AbstractThis study investigated the effects of two methods of ordering practice instances in the Merrill and Tennyson (1977) design for teaching coordinate concepts. Previous findings indicated that a response-sensitive presentation (presentation order based on the correctness of student's response) was more effective than a response-insensitive presentation (randomly ordered) with individualized, computer-assisted instruction. The present study examined whether these findings would generalize to a regular classroom environment. One hundred one first-grade students were randomly assigned to two treatment groups and a control group and were taught the coordinate concepts-mountains, hills, plains, and tablelands. Findings indicate that a response-sensitive presentation was more effective in teaching coordinate concepts than a response-insensitive presentation. The authors suggested that the Merrill and Tennyson design for teaching a single concept be modified to include a response-sensitive presentation rat...

Adaptive design strategies for selecting number and presentation order of examples in coordinate concept acquisition.

Adaptive design strategies for selecting number and presentation order of examples in coordinate concept acquisition.