Advancing Learner Autonomy in Tefl Via Collaborative Learning

Learner autonomy has been defined as \u27a capacity to control important aspects of one\u27s learning\u27(Benson, 2013, p. 852). In the teaching of additional languages, learner autonomy dates back at least to the 1970s. For instance, Trim, who was a leader in the teaching of additional languages in Europe, stated that a goal of language education was to: make the process of language learning more democratic by providing the con- ceptual tools for the planning, construction and conduct of courses closely geared to the needs, motivations and characteristics of the learner and enabling him [sic] so far as possible to steer and control his own progress. (1978, p. 1

Adaptive Guidance: Effects On Self-Regulated Learning In Technology-Based Training

Guidance provides trainees with the information necessary to make effective use of the learner control inherent in technology-based training, but also allows them to retain a sense of control over their learning (Bell & Kozlowski, 2002). One challenge, however, is determining how much learner control, or autonomy, to build into the guidance strategy. We examined the effects of alternative forms of guidance (autonomy supportive vs. controlling) on trainees’ learning and performance, and examined trainees’ cognitive ability and motivation to learn as potential moderators of these effects. Consistent with our hypotheses, trainees receiving adaptive guidance had higher levels of knowledge and performance than trainees in a learner control guidance. Controlling guidance had the most consistent positive impact on the learning outcomes, while autonomy supportive guidance demonstrated utility for more strategic outcomes. In addition, guidance was generally more effective for trainees with higher levels of cognitive ability and autonomy guidance served to enhance the positive effects of motivation to learn on the training outcomes

Neural Lyapunov Control

We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability. The framework consists of a learner that attempts to find the control and Lyapunov functions, and a falsifier that finds counterexamples to quickly guide the learner towards solutions. The procedure terminates when no counterexample is found by the falsifier, in which case the controlled nonlinear system is provably stable. The approach significantly simplifies the process of Lyapunov control design, provides end-to-end correctness guarantee, and can obtain much larger regions of attraction than existing methods such as LQR and SOS/SDP. We show experiments on how the new methods obtain high-quality solutions for challenging control problems.Comment: NeurIPS 201

Incorporating Problem-Based Learning Strategies to Develop Learner Autonomy and Employability Skills in Sports Science Undergraduates

This study investigated the impact of a 12-week problem-based learning (PBL) intervention on three psychological constructs (motivation, locus of control and self-esteem) linked to learner autonomy. Results indicated that there was a significant increase in the students’ intrinsic motivation (P<0.05) and a non-significant shift towards an internal locus of control after the intervention period. Students perceived the benefits of PBL to be the opportunity to work in teams and to consider a wider knowledge base. A short course of PBL was successful in developing learner autonomy and other key employability skills alongside the application of content knowledge

How Do Learners Interact with E-learning? Examining Patterns of Learner Control Behaviors

There has been significant debate in the literature on technology-mediated training about the appropriate role of learner control. We define learner control as giving trainees the ability to make choices about how they proceed through the learning environment. We explore two perspectives. First, we consider learners’ stated preferences for the extent of control in the learning environment. Second, we analyze the actual online learning behaviors of 518 trainees in a Fortune 500 organization. We compare a measure of learner control preferences to the most commonly used framework of learner control that comprises five dimensions: pace of instruction, sequence of topics, specific content covered, amount of advice/feedback provided, and type of media. We also compare the dimensionality of learner behaviors to this framework and examine the relationship between learner preferences and learner behaviors. Results suggest that fewer dimensions can capture both learner preferences and behaviors than what the literature currently suggests. Specifically, media control aligned with both pace and content control. The relationship between stated learner control preferences and learner control behaviors was relatively weak. However, we found support for the recently identified dimension of scheduling control and suggest a new learner control dimension of performance control, consistent with the importance of practice retrieval for learning

Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the Conditional Average Treatment Effect (CATE) function. Meta-algorithms build on base algorithms---such as Random Forests (RF), Bayesian Additive Regression Trees (BART) or neural networks---to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a new meta-algorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other, and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the meta-learners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our new X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods

The Impact of Learner Control on E-Learning Effectiveness: Towards a Theoretical Model

E-learning systems are changing education and organizational training considerably. With the advancement of online-based learning systems, learner control of the instructional process has emerged as a decisive factor inherent to technology-based learning. However, the conceptual work on the role of learner control in e-learning has not advanced sufficiently to predict how learner control impacts e-learning effectiveness. To extend the research on the role of learner control in e-learning, we derive a conceptual framework as a reference model, which is based on cognitive and motivational learning theories. We then apply our framework to review 58 articles on learner control during the period 1996-2013. Our findings reveal how different individual characteristics, as well as the characteristics of the course and learning environment moderate the impact of learner control on learning effectiveness. Our analysis provides new insight into the role of learner control for e-learning effectiveness, as well as directions for further research

Learning While Using an Instructional Simulation

Learner control is thought to be valuable by some scholars who believe that it allows learners to adapt instructions to their needs while reducing cognitive load (Mayer & Moreno, 2003). Although learner control offers some advantages to the learner, the importance of an instructor cannot be denied. In instructor-controlled settings the instructor provides guidance to the learners. Direct instructional guidance provides information to the learner that explains the concepts and procedures that are to be learned along with the instructional strategy support that is compatible with human cognitive architecture (Kirschner, Sweller, & Clark, 2006). This study compared the effects of learner-controlled simulation to instructor-guided presentation of an instructional simulation. Outcome variables were achievement, cognitive load, time-on-task, instructional efficiency, perceptions of learner control, and attitude for future use. Results of the study indicated no significant differences between the learner-controlled and instructor-guided treatments for achievement, cognitive load, or instructional efficiency. A significant difference was found between the treatments for time-on-task and the perception of learner control where participants in the learner-controlled group spent significantly less time completing the instruction and reported significantly higher learner-control than those in the instructor guidance with activity group

Effects of corpus-based instruction on phraseology in learner English

This study analyses the effects of data-driven learning (DDL) on the phraseology used by 223 English students at an Italian university. The students studied the genre of opinion survey reports through paper-based and hands-on exploration of a reference corpus. They then wrote their own report and a learner corpus of these texts was compiled. A contrastive interlanguage analysis approach (Granger, 2002) was adopted to compare the phraseology of key items in the learner corpus with that found in the reference corpus. Comparison is also made with a learner corpus of reports produced by a previous cohort of students who had not used the reference corpus. Students who had done DDL tasks used a wider range of genre-appropriate phraseology and produced a lower number of stock phrases than those who had not. The study also finds evidence that students use more phrases encountered in paper-based concordancing tasks than in hands-on tasks.Unlike in previous DDL studies, observations of the learning of a specific text-type through DDL in the present study are based on the comparison with both a control learner corpus and an expert corpus.The study also considers the use of DDL with a large class size