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Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important issue and has to be investigated to guarantee that the questionnaire really assesses what the learning style theory aims at. In this paper, we focus on the Index of Learning Styles (ILS), a 44‐item questionnaire to identify learning styles based on Felder‐ Silverman learning style model. The aim of this paper is to analyse data gathered from ILS by a data‐driven approach in order to investigate relationships within the learning styles. Results, obtained by Multiple Correspondence Analysis and cross‐validated by correlation analysis, show the consistent dependencies between some learning styles and lead then to conclude for scarce validity of the ILS questionnaire. Some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of the ILS questionnaire. Both the results and the comparisons show the effectiveness of data‐driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for effective, precise and accurate modelling.

The purpose of the study is to examine how operating efficiencies from incentive alignment compensate for rent extraction in family firms. The author asks whether ownership (1) improves operating efficiencies to increase firm value, (2) positively affects related-party transactions (RPTs), or (3) destroys firm value. Finally, the author assesses whether the incentive effect dominates the entrenchment effect.

This study employs a panel of 333 listed family firms (and 185 nonfamily firms) and handles endogeneity using a dynamic panel system GMM and panel VAR.

Ownership decreases discretionary expenses and increases asset utilization to add firm value. The efficiency gains generate more value in family firms, especially majority-held ones, than in nonmajority ones. However, ownership is also related to increased RPTs (especially dubious loans/guarantees), reducing firm value. RPTs destroy value more severely in the family (or group) firms than in nonfamily (nongroup) firms. It could be why ownership's positive impact on value is lower in family firms than in nonfamily firms. Overall, the incentive effect dominates the entrenchment effect and is robust to controlling private benefits of control in the dynamic ownership-value model.

(1) A family firm's ownership may not be optimal. (2) The firm's long-term commitment as a dynasty limits the scale of expropriation yet sustains impetus for long-term value creation. The paradox partly explains why large family holdings and firm-specific investments endure over generations. (3) This way, large ownership substitutes weak investor protection in India despite tunneling as skin in the game provides necessary investor confidence. (4) Future studies can examine whether extraction varies with family generations and how family characteristics affect the incentive effects.

(1) Concentrated ownership may not be a wrong policy choice in emerging markets to draw firm-specific investments. (2) Investors, auditors, or creditors must pay closer attention to loans/guarantees. (3) More vigorous enforcement, auditor scrutiny, and board oversight are needed.

Family firms are not necessarily a bad organization type that destroys investor wealth. They can be valuably efficient due to their ownership and wealth concentration, and frugality. They matter in the economic growth of a developing market like India.

(1) Extends ownership-performance research to family firms and shows that although ownership facilitates tunneling, the incentive effect dominates; (2) family ownership is not impacted by firm value; (3) family ownership levels reduce discretionary expenses and increase asset utilization to create added value, especially in majority-held family firms; (4) RPTs and loans/guarantees increase with ownership; (5) value erosion from RPTs is higher in family (group) firms than in other firms.

Researchers frequently come across teachers who distrust a learning environment as embodying the beliefs of the designers and not their own pedagogy. Following the lead provided by user modelling work carried out in the field of human‐computer interaction, there has been much research on student modelling and adaptivity to individual learners; however, the role of the teacher as the manager of the learning process and hence a much more significant user of a learning environment has been ignored. This paper discusses the need for a human teacher model in any computer‐based learning environment and recommends configurable, incremental and re‐structurable contributive learning environments (CIRCLE) architecture to ensure wider acceptance and greater reuse of the phenomenal creative effort that goes into designing a good learning environment.

Describes Byzantium, an intelligent tutoring system for teaching the concepts and skills of accounting. The generic design philosophy of Byzantium and its associated intelligent tutoring tools are described, together with commentary that places Byzantium in the tradition of the adaptive teaching machines and conversational tutorial systems (SAKI and CASTE) developed by Gordon Pask.

The aim of this study is to understand a family firm's choice of related-party transaction (RPT) types and analyze their value impacts to separate the abusive from benign RPTs.

It uses a 10-year panel of BSE-listed 378 family (and 200 non-family) firms. The fixed effects, logit and difference-in-difference (DID) models help examine value effects, propensity and persistence of harmful RPTs.

Loans/guarantees (irrespective of counterparties) destroy firm value. Capital asset RPTs decrease the firm value but enhance value when undertaken with holding parties. Operating RPTs increase firm value and profitability. They improve asset utilization and reduce discretionary expenses (especially when made with controlled entities). Family firms have larger loans/guarantees and capital asset volumes but have smaller operating RPTs than non-family firms. They are less likely to undertake loans/guarantees (and even operating RPTs) and more capital RPTs vis-à-vis non-family firms. Family firms persist with dubious loans/guarantees but hold back beneficial operating RPTs, despite RPTs being in investor cross-hairs amid the Satyam scam.

Rent extractability and counterparty incentives supplement each other. (1) The higher extractability of related-party loans and guarantees (RPLGs) dominates the lower extraction incentives of controlled parties. (2) Holding parties' bringing assets, providing a growth engine and adding value dominate their higher extraction incentives (3) The big gains to the operational efficiency come from operating RPTs with controlled parties, generally operating companies in the family house. (4) Dubious RPTs seem more integral to family firms' choices than non-family firms. (5) Counterparty incentives behind the divergent use of RPTs deserve more research attention. Future studies can give more attention to how family characteristics affect divergent motives behind RPTs.

First, the study does not single out family firms for dubious use of all RPTs. Second, investors, auditors or creditors must pay close attention to RPLGs as a special expropriation mechanism. Third, operating RPTs (and capital RPTs with holding parties) benefit family firms. However, solid procedural safeguards are necessary. Overall, results may help clarify the dilemma Indian regulators face in balancing the abusive and business sides of RPTs.

The study fills the gap by arguing why some RPTs may be dubious or benign and then shows how RPTs' misuse depends on counterparty types. It shows operating RPTs enhance operating efficiencies on several dimensions and that benefits may vary with counterparty types. It also presents the first evidence that family firms favor dubious RPTs more and efficient RPTs less than non-family firms.