@article{Reich2019,

title = {The MOOC pivot},

author = {Justin Reich and José A. Ruipérez-Valiente},

url = {http://dx.doi.org/10.1126/science.aav7958},

doi = {10.1126/science.aav7958},

issn = {1095-9203},

year  = {2019},

date = {2019-01-11},

journal = {Science},

volume = {363},

number = {6423},

pages = {130–131},

abstract = {When massive open online courses (MOOCs) first captured global attention in 2012, advocates imagined a disruptive transformation in postsecondary education. Video lectures from the world's best professors could be broadcast to the farthest reaches of the networked world, and students could demonstrate proficiency using innovative computer-graded assessments, even in places with limited access to traditional education. But after promising a reordering of higher education, we see the field instead coalescing around a different, much older business model: helping universities outsource their online master's degrees for professionals (1). To better understand the reasons for this shift, we highlight three patterns emerging from data on MOOCs provided by Harvard University and Massachusetts Institute of Technology (MIT) via the edX platform: The vast majority of MOOC learners never return after their first year, the growth in MOOC participation has been concentrated almost entirely in the world's most affluent countries, and the bane of MOOCs—low completion rates (2)—has not improved over 6 years.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lemke2019,

title = {“When You Use Social Media You Are Not Working”: Barriers for the Use of Metrics in Social Sciences},

author = {Steffen Lemke and Maryam Mehrazar and Athanasios Mazarakis and Isabella Peters},

url = {https://doi.org/10.3389/frma.2018.00039},

doi = {10.3389/frma.2018.00039},

issn = {2504–0537},

year  = {2019},

date = {2019-01-08},

journal = {Frontiers in Research Metrics and Analytics},

volume = {3},

pages = {39},

abstract = {The Social Sciences have long been struggling with quantitative forms of research assessment – insufficient coverage in prominent citation indices and overall lower citation counts than in STM subject areas have led to a widespread weariness regarding bibliometric evaluations among social scientists. Fueled by the rise of the social web, new hope is often placed on alternative metrics that measure the attention scholarly publications receive online, in particular on social media. But almost a decade after the coining of the term altmetrics for this new group of indicators, the uptake of the concept in the Social Sciences still seems to be low. Just like with traditional bibliometric indicators, one central problem hindering the applicability of altmetrics for the Social Sciences is the low coverage of social science publications on the respective data sources – which in the case of altmetrics are the various social media platforms on which interactions with scientific outputs can be measured. Another reason is that social scientists have strong opinions about the usefulness of metrics for research evaluation which may hinder broad acceptance of altmetrics too. 

We conducted qualitative interviews and online surveys with researchers to identify the concerns which inhibit the use of social media and the utilization of metrics for research evaluation in the Social Sciences. By analyzing the response data from the interviews in conjunction with the response data from the surveys, we identify the key concerns that inhibit social scientists from (1) applying social media for professional purposes and (2) making use of the wide array of metrics available. 

Our findings show that aspects of time consumption, privacy, dealing with information overload, and prevalent styles of communication are predominant concerns inhibiting Social Science researchers from using social media platforms for their work. Regarding indicators for research impact we identify a widespread lack of knowledge about existing metrics, their methodologies and meanings as a major hindrance for their uptake through social scientists. The results have implications for future developments of scholarly online tools and show that researchers could benefit considerably from additional formal training regarding the correct application and interpretation of metrics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Orben2019,

title = {The association between adolescent well-being and digital technology use},

author = {Amy Orben and Andrew K. Przybylski},

url = {https://doi.org/10.1038/s41562-018-0506-1},

doi = {10.1038/s41562-018-0506-1},

issn = {2397-3374},

year  = {2019},

date = {2019-01-14},

journal = {Nature Human Behaviour},

abstract = {The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4{37d1f293241a1edd19b097ce37fa29bd44d887a41b5283a0fc9485076e078306} of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}