Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ju Fan ◽  
Yuanchun Jiang ◽  
Yezheng Liu ◽  
Yonghang Zhou
Keyword(s):  
Personalized Learning ◽  
Learning Preferences ◽  
Attention Mechanism ◽  
Real World Data ◽  
Attention Network ◽  
Learning Behaviors ◽  
Content Type ◽  
Word Level ◽  
Multiple Data ◽  
Sentence Level

PurposeCourse recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.Design/methodology/approachThe study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.FindingsThe main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.Practical implicationsThe findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.Originality/valueThis study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

EmmDocClassifier: Efficient Multimodal Document Image Classifier for Scarce Data

2022 ◽  
Author(s):  
Shrinidhi Kanchi ◽  
Alain Pagani ◽  
Hamam Mokayed ◽  
Marcus Liwicki ◽  
Didier Stricker ◽  
...  
Keyword(s):  
Visual Cues ◽  
Reduction Rate ◽  
Document Classification ◽  
Document Image ◽  
Analysis Pipeline ◽  
Attention Network ◽  
Word Level ◽  
Novel Approach ◽  
The Neural Network ◽  
Sentence Level

Document classification is one of the most critical steps in the document analysis pipeline. There are two types of approaches for document classification, known as image-based and multimodal approaches. The image-based document classification approaches are solely based on the inherent visual cues of the document images. In contrast, the multimodal approach co-learns the visual and textual features, and it has proved to be more effective. Nonetheless, these approaches require a huge amount of data. This paper presents a novel approach for document classification that works with a small amount of data and outperforms other approaches. The proposed approach incorporates a hierarchical attention network(HAN) for the textual stream and the EfficientNet-B0 for the image stream. The hierarchical attention network in the textual stream uses the dynamic word embedding through fine-tuned BERT. HAN incorporates both the word level and sentence level features. While the earlier approaches rely on training on a large corpus (RVL-CDIP), we show that our approach works with a small amount of data (Tobacco-3482). To this end, we trained the neural network at Tobacco-3428 from scratch. Thereby, we outperform state-of-the-art by obtaining an accuracy of 90.3%. This results in a relative error reduction rate of 7.9%.

On the potential of a graph attention network in money laundering detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guang-Yih Sheu ◽  
Chang-Yu Li
Keyword(s):  
Support Vector Machines ◽  
Money Laundering ◽  
Naive Bayes ◽  
Attention Mechanism ◽  
Support Vector ◽  
Bayes Classifier ◽  
Attention Network ◽  
Content Type ◽  
Detection Tool ◽  
Vector Machines

Purpose In a classroom, a support vector machines model with a linear kernel, a neural network and the k-nearest neighbors algorithm failed to detect simulated money laundering accounts generated from the Panama papers data set of the offshore leak database. This study aims to resolve this failure. Design/methodology/approach Build a graph attention network having three modules as a new money laundering detection tool. A feature extraction module encodes these input data to create a weighted graph structure. In it, directed edges and their end vertices denote financial transactions. Each directed edge has weights for storing the frequency of money transactions and other significant features. Social network metrics are features of nodes for characterizing an account’s roles in a money laundering typology. A graph attention module implements a self-attention mechanism for highlighting target nodes. A classification module further filters out such targets using the biased rectified linear unit function. Findings Resulted from the highlighting of nodes using a self-attention mechanism, the proposed graph attention network outperforms a Naïve Bayes classifier, the random forest method and a support vector machines model with a radial kernel in detecting money laundering accounts. The Naïve Bayes classifier produces second accurate classifications. Originality/value This paper develops a new money laundering detection tool, which outperforms existing methods. This new tool produces more accurate detections of money laundering, perfects warns of money laundering accounts or links and provides sharp efficiency in processing financial transaction records without being afraid of their amount.

Improving recommender systems’ performance on cold-start users and controversial items by a new similarity model

2016 ◽  
Vol 12 (2) ◽  
pp. 126-149 ◽  
Author(s):  
Masoud Mansoury ◽  
Mehdi Shajari
Keyword(s):  
Real World ◽  
Design Methodology ◽  
Cold Start ◽  
Selection Function ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
User Similarity ◽  
Active User ◽  
Similarity Model

Purpose This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users). Design/methodology/approach A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results. Findings Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items. Originality/value In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

Deaf and Hard-of-hearing Users Evaluating Designs for Highlighting Key Words in Educational Lecture Videos

2021 ◽  
Vol 14 (4) ◽  
pp. 1-24
Author(s):  
Sushant Kafle ◽  
Becca Dingman ◽  
Matt Huenerfauth
Keyword(s):  
Experimental Study ◽  
Key Words ◽  
Hard Of Hearing ◽  
Experimental Studies ◽  
Design Parameters ◽  
Educational Video ◽  
Word Level ◽  
Sentence Level ◽  
First Occurrence ◽  
Educational Videos

There are style guidelines for authors who highlight important words in static text, e.g., bolded words in student textbooks, yet little research has investigated highlighting in dynamic texts, e.g., captions during educational videos for Deaf or Hard of Hearing (DHH) users. In our experimental study, DHH participants subjectively compared design parameters for caption highlighting, including: decoration (underlining vs. italicizing vs. boldfacing), granularity (sentence level vs. word level), and whether to highlight only the first occurrence of a repeating keyword. In partial contrast to recommendations in prior research, which had not been based on experimental studies with DHH users, we found that DHH participants preferred boldface, word-level highlighting in captions. Our empirical results provide guidance for the design of keyword highlighting during captioned videos for DHH users, especially in educational video genres.

Informal learning behaviors and the role of workplace support: preliminary findings from an India-USA workforce

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nidheesh Joseph ◽  
E. Sownthara Rajan
Keyword(s):  
Informal Learning ◽  
Organizational Support ◽  
Amazon Mechanical Turk ◽  
Learning Behaviors ◽  
Content Type ◽  
Workplace Support ◽  
Job Support ◽  
The Usa ◽  
Job Role

Purpose (mandatory) The purpose of this paper is to study engagement of employees in informal learning behaviors (ILBs) and to understand the role of workplace support (organizational support, supervisor support and job support) in facilitating such behaviors. Design/methodology/approach (mandatory) The study uses descriptive design with data collected through voluntary non-probability sampling method of 58 employees from India and the USA through Amazon Mechanical Turk. Findings (mandatory) Preliminary findings suggest that 81% of the employees are likely to engage in ILBs and 65.5% agreed to have received workplace support. Employees from India rate their workplace support as higher and are more likely to engage in ILBs than those from the USA. Originality/value (mandatory) This study contributes to workplace informal learning literature and highlights the need for more studies on workforce ILBs across multiple countries and job role variations.

Can earning much lead to doing good? Firm performance implications for corporate social responsibility

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Siyu Hou ◽  
Zhaoyang Guo ◽  
Chuangneng Cai ◽  
Xiaobo Jiao
Keyword(s):  
Corporate Social Responsibility ◽  
Firm Performance ◽  
Social Responsibility ◽  
State Ownership ◽  
Data Sets ◽  
Content Type ◽  
Multiple Data ◽  
Instrumental Variable Regression ◽  
Multiple Data Sets ◽  
Corporate Social

Purpose The purpose of this study is to examine the influence of firm performance on corporate social responsibility (CSR) and its possible moderating effect. Despite the significance of CSR, there remains an extensive debate about how it is affected by firm performance. Design/methodology/approach The conceptual model is mainly built on goal-setting theory. Based on archival data from multiple data sets on 1,650 companies, collected from 2010 to 2017, the hypotheses are tested using the two-stage instrumental variable regression method. Findings There is an inverted U-shaped relationship between firm performance and CSR that first increases and then decreases. In addition, considering the boundary conditions, state ownership makes the inverted U-shaped curve steeper, while high executive wage concentration makes the inverted U-shaped curve flatter. Research limitations/implications This study harmonizes the traditional contradictory findings of the influence of firm performance on CSR, that is, it supports a positive, negative or neutral relationship between the two. Originality/value This research provides a necessary structure for the CSR literature. By delving deeply into the relationship between firm performance and CSR, it enables scholars to better address the critical management question of whether earning more will lead to doing good.

scholarly journals Individual goals and social preferences in operational decisions

2019 ◽  
Vol 39 (1) ◽  
pp. 116-137 ◽  
Author(s):  
Nienke Hofstra ◽  
Wout Dullaert ◽  
Sander De Leeuw ◽  
Eirini Spiliotopoulou
Keyword(s):  
Decision Making ◽  
Social Preferences ◽  
Operational Planning ◽  
Future Research ◽  
Monetary Incentives ◽  
Content Type ◽  
Planning Decisions ◽  
Multiple Data ◽  
Human Decision ◽  
Insight Into

Purpose The purpose of this paper is to develop propositions explaining the influence of individual goals and social preferences on human decision making in transport planning. The aim is to understand which individual goals and social preferences planners pursue and how these influence planners’ decisions. Design/methodology/approach Propositions are developed based on investigation of decision making of transport planners in a Dutch logistics service provider using multiple data collection methods. Findings The study shows how decision making of transport planners is motivated by individual goals as well as social preferences for reciprocity and group identity. Research limitations/implications Further research including transaction data analysis is needed to triangulate findings and to strengthen conclusions. Propositions are developed to be tested in future research. Practical implications Results suggest that efforts to guide planners in their decision making should go beyond traditional (monetary) incentives and consider their individual goals and social preferences. Moreover, this study provides insight into why transport planners deviate from desired behaviour. Originality/value While individual decision making plays an essential role in operational planning, the factors influencing how individuals make operational planning decisions are not fully understood.

Rapid video assessment for monitoring testing facility fraud

2018 ◽  
Vol 35 (8) ◽  
pp. 1508-1518
Author(s):  
Rosembergue Pereira Souza ◽  
Luiz Fernando Rust da Costa Carmo ◽  
Luci Pirmez
Keyword(s):  
Processing Time ◽  
Threshold Value ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Video Assessment ◽  
Technical Requirements ◽  
Testing Facility ◽  
Rapid Processing ◽  
Temporal Differencing

Purpose The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant. Design/methodology/approach An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion. Findings With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed. Originality/value Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

scholarly journals Understanding Syntactic and Semantic Errors in the Composition Writing of Jordanian EFL Learners

2017 ◽  
Vol 6 (6) ◽  
pp. 158
Author(s):  
Yazan Shaker Almahameed ◽  
May Al-Shaikhli
Keyword(s):  
Foreign Language ◽  
Language Learners ◽  
Semantic Knowledge ◽  
Resumptive Pronouns ◽  
Foreign Language Learners ◽  
Word Level ◽  
Sentence Level ◽  
Native Speakers Of English ◽  
Semantic Errors ◽  
Verb Tense

The current study aimed at investigating the salient syntactic and semantic errors made by Jordanian English foreign language learners as writing in English. Writing poses a great challenge for both native and non-native speakers of English, since writing involves employing most language sub-systems such as grammar, vocabulary, spelling and punctuation. A total of 30 Jordanian English foreign language learners participated in the study. The participants were instructed to write a composition of no more than one hundred and fifty words on a selected topic. Essays were collected and analyzed statistically to obtain the needed results. The results of the study displayed that syntactic errors produced by the participants were varied, in that eleven types of syntactic errors were committed as follows; verb-tense, agreement, auxiliary, conjunctions, word order, resumptive pronouns, null-subject, double-subject, superlative, comparative and possessive pronouns. Amongst syntactic errors, verb tense errors were the most frequent with 33%. The results additionally revealed that two types of semantic errors were made; errors at sentence level and errors at word level. Errors at word level outstripped by far errors at sentence level, scoring respectively 82% and 18%. It can be concluded that the syntactic and semantic knowledge of Jordanian learners of English is still insufficient.

scholarly journals Product reputation mining: bring informative review summaries to producers and consumers

2019 ◽  
Vol 16 (2) ◽  
pp. 359-380
Author(s):  
Zhehua Piao ◽  
Sang-Min Park ◽  
Byung-Won On ◽  
Gyu Choi ◽  
Myong-Soon Park
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Word Level ◽  
Sentence Level ◽  
Survey Results ◽  
Statistical Studies ◽  
Sentiment Orientation ◽  
Level Method ◽  
Points Of View ◽  
Graph Based Model

Product reputation mining systems can help customers make their buying decision about a product of interest. In addition, it will be helpful to investigate the preferences of recently released products made by enterprises. Unlike the conventional manual survey, it will give us quick survey results on a low cost budget. In this article, we propose a novel product reputation mining approach based on three dimensional points of view that are word, sentence, and aspect?levels. Given a target product, the aspect?level method assigns the sentences of a review document to the desired aspects. The sentence?level method is a graph-based model for quantifying the importance of sentences. The word?level method computes both importance and sentiment orientation of words. Aggregating these scores, the proposed approach measures the reputation tendency and preferred intensity and selects top-k informative review documents about the product. To validate the proposed method, we experimented with review documents relevant with K5 in Kia motors. Our experimental results show that our method is more helpful than the existing lexicon?based approach in the empirical and statistical studies.

Sign in / Sign up

Export Citation Format

Share Document