Design Principle of an Automatic Engagement Estimation System in a Synchronous Distance Learning Practice

Engagement is an essential component of the learning processes associated with positive learning outcomes. Measuring learner engagement in learning processes is vital to providing insights for enhancing learning activities. Because the learning paradigm has shifted to enable more distance learning practices, machine learning-based automatic engagement estimation methods have been proposed as a new way to measure learner engagement. Nevertheless, most existing methods are built standalone and have yet to be integrated into actual distance learning practice. Furthermore, implementing automatic engagement estimation should ensure technological and ethical impact responsibilities. This article proposes a design principle for the end-to-end integration of automatic engagement in distance learning practice. The MeetmEE system design was introduced to measure learners’ emotional engagement in a synchronous distance learning practice. The MeetmEE prototype was deployed in a pilot experiment to evaluate the MeetmEE system design. Finally, the user evaluation results are considered to construct the design principle of ethical implementation. The design principle for implementing the automatic engagement estimation incorporates technical and operational measures

A hybrid TOA and RSS-based factor graph for wireless geolocation technique

This paper proposes a new hybrid time-of-arrival (TOA) and received-signal-strength (RSS)-based factor graph (TRFG) for wireless geolocation technique. The TOA-based FG (TFG) provides rough estimated position which is used to select the most appropriate monitoring spot positions, i.e., at least four monitoring spots surrounding the target, and initial target position for RSS-based FG (RFG) technique. The performance of the proposed technique is verified through making comparison with the conventional TFG-only technique suffering from imperfect time synchronization, as well as with the idealistic RFG technique, in terms of the root mean squared error (RMSE) of the estimate. It is shown that the RFG technique utilizing the result of TFG achieves close performance to the idealistic RFG technique where the optimal monitoring spots are assumed to be always correctly identified. Hence, the proposed technique outperforms the TFG-only technique in terms of estimation accuracy

Achieving accurate geo-location detection using joint RSS-DOA factor graph technique

This paper proposes a detection technique based on factor graph (FG) to estimate the position of radio wave emitter. To obtain accurate estimation, we combine received signal strength (RSS) and direction of arrival (DOA) schemes into a single factor graph, called joint RSS-DOA, where soft information as mean and variance of the estimated target position are exchanged between the two schemes. The performance of DOA in this paper is used to modify variance approximation of the target location. We introduce the weighting factors for RSS and for DOA to avoid the soft information of DOA factor graph be ignored. With the proposed technique, the complexity is kept low, because only mean and variance are exchanged between the factor nodes. Ray-tracing data is used in outdoor application to create power delay profile (PDP) for the RSS-based factor graph and evaluate 5, 10, and 20 training points. The results confirmed that proposed joint RSS-DOA has best accuracy in detection compared to RSS-based or DOA-based only factor graph. To the best of our knowledge, we are the first showing successful results of FG based geo-location using Ray-tracing data

A PTDOA-DRSS hybrid factor graph-based unknown radio wave geolocation

Abstract We propose a hybrid Pythagorean Time Difference of Arrival and Differential Received Signal Strength based Factor Graph (PTDOA-DRSS-FG) to estimate the location of unknown radio wave emitter in outdoor environments. The term of “unknown” indicates that the knowledge of neither time of departure (TOD) nor absolute transmit power of the radio wave emitter are required. The PTDOA-FG can eliminate the necessity of TOD knowledge of the target signal transmission and DRSS-FG can eliminate the necessity of the knowledge of target absolute transmit power. However, PTDOA-FG alone requires perfect time synchronism between sensors, which is difficult in practice. On the other hand, DRSS-FG alone requires the most suitable monitoring spots. In this paper, PTDOA-FG is used to provide the rough estimation of the target position to select the most suitable monitoring spots for DRSS-FG technique. It is shown that, in terms of root mean square error (RMSE) vs. iteration times, the achieved RMSE of the proposed technique is better than the PTDOA-FG alone and very close to the DRSS reference, i.e., the DRSS-FG technique with the idealistic monitoring spots. Performing the proposed technique in the framework of factor graph (FG) does not require excessive computational complexity due to the fact that using the Gaussian distribution model, it uses mean and variance only with the sum-product algorithm