text:
\section{Introduction and related work}
As the number and diversity of massive open online courses (MOOCs) continues to grow, researchers, teachers, and educational
technologists are starting to explore innovative ways of helping more students who participate in these courses to persist
longer and learn more. While some interventions (e.g., email reminders \cite{WhitehillEtAl2015}) may incur very little cost
once the core infrastructure for conducting them is in place, other types of interventions -- such as a course
staff-member actively reaching out to a learner
to ask her/him how she/he is doing \cite{simpson2004impact,firmin2014case},
or an instructor responding substantively within a discussion forum to a comment the student
had written \cite{WongEtAl2016} -- may have high variable costs and be very time-intensive. An automatic detector that could predict automatically
-- based on demographics or by analyzing subtle cues in a learner's interaction history with the MOOC courseware --
which learners are in danger of ``dropping out'' from the MOOC and which will likely succeed, would be a valuable tool for making
smart decisions about who receives a particular intervention.

{\bf Related work}: Within the fields of learning analytics and educational data mining,
the possibility of creating automatic MOOC dropout detectors has generated considerable interest within the past few years.
Existing approaches vary across several dimensions including the \emph{features}
used for classification, the \emph{architecture} used for training and testing, and the \emph{training set} used to optimize
the classifier parameters -- see Table \ref{tbl:lit_review} for a synopsis of prior work.
The most popular feature representations include clickstream logs, natural language processing (NLP)
of discussion forum content, and social network metrics. In terms of architecture, most prior approaches have
used generalized linear models (including logistic regression and linear SVMs), survival analysis (e.g., Cox proportional hazard
model), and logistic regression. A third dimension of variability is the \emph{training setting} that
describes the \emph{source} of the training data -- e.g., the same course, a prior instance of the same course,
or a different course altogether -- relative to the \emph{target use} of the classifier once it has been
trained: most research on MOOC dropout prediction to-date has focused
on training and testing on data sampled from the same MOOC.
\begin{table*}
\begin{center}
\begin{tabular}{l|c|l|l|l}
\multicolumn{5}{c}{\bf Survey of Prior Research on MOOC Dropout Prediction}\\\hline
{\bf Study} & {\bf \#MOOCs} & {\bf Features} & {\bf Architecture} & {\bf Training setting} \\\hline\hline
Balakrishnan \& Coetzee~\cite{BalakrishnanBerkeley2013} & 1 & Clickstream & HMM + SVM & Same course\\\hline
Boyer \& Veeramachaneni~\cite{boyer2015transfer} & 3 & Clickstream & TL+LR & \begin{tabular}{@{}l}Different offering\\In-situ\end{tabular} \\\hline
Coleman et al.~\cite{ColemanEtAl2015} & 1 & Clickstream & LDA+LR & Same course\\\hline
Crossley et al.~\cite{crossley2016combining} & 1 & Clickstream; NLP & DFA & Same course\\\hline
Fei \& Yeung~\cite{fei2015temporal} & 2 & Clickstream & RNN & Same course\\\hline
He et al.~\cite{HeEtAl2015} & 2 & Clickstream & Smoothed LR  & Different offering\\\hline
Jiang et al.~\cite{JiangEtAl2014} & 1 & Social network; grades & LR & Same course\\\hline
Kizilcec et al.~\cite{KizilcecHalawa2015,HalawaEtAl2014} & 20 & Clickstream & LR & \begin{tabular}{@{}l}Different course\\Same course\end{tabular} \\\hline
Kloft et al.~\cite{KloftEtAl2014} & 1 & Clickstream & SVM & Same course \\\hline
Koedinger et al.~\cite{koedinger2015learning} & 1 & Clickstream; grades & LR & Same course \\ \hlin
Robinson et al.~\cite{robinson2016forecasting} & 1 & Survey; NLP & LR & Same course\\\hline
Rose et al.~\cite{YangEtAl2014,rose2014social} & 1 & Forum; social network & SA & Same course \\ \hlin
Stein \& Allione~\cite{SteinAllione2014} & 1 & Clickstream; survey & SA & Same course \\ \hlin
Taylor et al.~\cite{TaylorEtAl2014} & 1 & Clickstream & LR & Same course \\\hline
Whitehill et al.~\cite{WhitehillEtAl2015} & 10 & Clickstream & LR & Different course \\\hline
Xing et al.~\cite{xing2016temporal} & 1 & Clickstream; social network & PCA+\{BN,DT\} & Same course\\\hline
Ye \& Biswas~\cite{YeBiswas2014} & 1 & Clickstream & LR & Same course \\ \hlin
{\bf Our paper} & 40 & Clickstream & \{LR, DNN\} & \begin{tabular}{@{}l}Same course\\In-situ\\Different course\end{tabular} \\\hline
\end{tabular}
\end{center}
\caption{Survey of prior literature on MOOC dropout prediction. For the architecture, we use the following
abbreviations: Bayesian network (BN), decision tree (DT), deep neural network (DNN), discriminant function
analysis (DFA), hidden Markov model (HMM), latent Dirichlet allocation (LDA), logistic regression (LR),
principal component analysis (PCA), recurrent neural network (RNN), support vector machine (SVM), survival analysis (SA),
and transfer learning (TL). Architecture $a+b$ means methods $a$ and $b$ were used in conjunction; $\{a,b\}$ means
that $a$ or $b$  were used as alternatives.}
\label{tbl:lit_review}
\end{table*}

When designing and implementing educational interventions that depend on automatic MOOC dropout predictors to
predict which students are in danger of failing, it is important  to know how accurate such predictors are.
In order to obtain reliable accuracy estimates, it is crucial to ensure that the method used for evaluating
the classifier is consistent with the manner it will be used for the actual intervention.
Live MOOC interventions require dropout predictors that are operational at or near the \emph{beginning} of a MOOC
while students can still benefit from receiving an intervention. But producing such a predictor
can be difficult because the target values which
indicate whether each student dropped out or completed the MOOC, and which are usually required for training a classifier
with standard supervised learning approaches, typically become available only at the \emph{end} of a MOOC --
at which point any intervention
is moot.

To-date, most prior research on MOOC dropout detection has largely ignored this issue and
both trained and tested on student data sampled from the same course (``post-hoc''), likely because dropout prediction
was a new field and the focus was on exploring different feature representations and classification architectures.
The study we present in this paper seeks to fill this gap.

{\bf Contributions}: (1) In this paper
we compare the accuracy of a standard dropout prediction architecture --
clickstream features classified by logistic regression -- across a variety of different training settings
in order to understand better the tradeoff between accuracy and practical deployability of the classifier.
The dataset we use for training and evaluation consists of 40 popular MOOCs from HarvardX that span
a variety of academic disciplines including social science, humanities, STEM, and health sciences.
As part of this evaluation, (2) we also explore a novel scheme (similar to the \emph{in-situ} approach proposed by
\cite{boyer2015transfer}) for training a classifier \emph{while} a course is ongoing using \emph{proxy labels}
and show that its performance is surprisingly good even compared to the overly optimistic \emph{post-hoc} training
paradigm. Finally, (3) we propose and evaluate a novel MOOC dropout architecture based on fully-connected, feed-forward neural
networks that are trained at each week in a MOOC. We provide evidence that much deeper (5 hidden layers) feature
representations than have previously been explored can lead to statistically significantly higher prediction accuracy.

\section{Dataset}
The experiments and analyses in this paper are based on data from 40 MOOCs from HarvardX -- see Table \ref{tbl:mooc_stats} which
lists the code name of each course; the year and term (T1, T2, and T3 are  northern hemisphere spring, summer, and fall, respectively)
when the course was offered; the academic discipline; number of registered participants;  and the number of participants who certified.
Each MOOC has a \emph{launch date} (which we call $T_{0\%}$) when the first materials -- e.g., lecture videos, discussion forums, etc. -- are released,
as well as an \emph{end date} when certificates are issued.

\begin{table}
\footnotesize
\begin{tabular}{l|l|c|r|r}
\multicolumn{5}{c}{\bf List of MOOCs}\\\hline
{\bf Course} & {\bf Year} & {\bf Field} & {\bf \#Part.} & {\bf \#Cert.}\\\hline
1368.1x & 2014T3 & SocialSci  & 2335 & 387\\
1368.3x & 2015T2 & SocialSci  & 1278 & 395\\
AmPoX.4 & 2015T1 & Hum  & 4348 & 344\\
AmPoX.5 & 2015T2 & Hum  & 2296 & 195\\
AmPoX.6 & 2016T2 & SocialSci  & 2810 & 45\\
BUS5.1x & 2014T1 & SocialSci  & 9386 & 575\\
BUS5.1x & 2015T3 & SocialSci  & 4363 & 173\\
EMC2x & 2015T1 & SocialSci  & 8257 & 196\\
ENGSCI137x & 2016T2 & STEM  & 7356 & 339\\
ER22.1x & 2014T1 & SocialSci  & 23972 & 2375\\
GSE2x & 2014T2 & SocialSci  & 23740 & 3847\\
HDS3221.1x & 2016T1 & Hum  & 17949 & 258\\
HKS101A & 2015T3 & SocialSci  & 9849 & 599\\
HKS211.2x & 2015T3 & SocialSci  & 3872 & 791\\
HLS2x & 2015T1 & SocialSci  & 10814 & 2190\\
HLS3x & 2015T1 & SocialSci  & 2468 & 354\\
MCB63X & 2015T3 & STEM  & 11225 & 561\\
MCB64.1x & 2016T2 & STEM  & 6984 & 268\\
PH210x & 2014T1 & HealthSci  & 6390 & 745\\
PH231x & 2016T1 & HealthSci  & 4372 & 414\\
PH525.1x & 2015T1 & HealthSci  & 14300 & 1804\\
PH525.2x & 2015T1 & HealthSci  & 5420 & 869\\
PH525.3x & 2015T1 & HealthSci  & 4690 & 464\\
PH525.5x & 2015T3 & HealthSci  & 1410 & 120\\
PH525x & 2014T1 & HealthSci  & 10705 & 628\\
PH556 & 2015T3 & HealthSci  & 6346 & 900\\
SPU30x & 2014T2 & STEM  & 5611 & 941\\
SW12.10x & 2015T1 & SocialSci  & 5176 & 1246\\
SW12.2x & 2014T1 & SocialSci  & 8660 & 2085\\
SW12.3x & 2014T1 & SocialSci  & 5051 & 1825\\
SW12.4x & 2014T1 & SocialSci  & 4979 & 1527\\
SW12.5x & 2014T2 & SocialSci  & 3466 & 1458\\
SW12.6x & 2014T2 & SocialSci  & 4011 & 1383\\
SW12.7x & 2014T3 & SocialSci  & 3749 & 1391\\
SW12.8x & 2014T3 & SocialSci  & 3677 & 1392\\
SW12.9x & 2014T3 & SocialSci  & 3569 & 1380\\
SW25x & 2014T1 & HealthSci  & 6444 & 1246\\
SW25x.1 & 2015T1 & HealthSci  & 3377 & 453\\
USW30x & 2016T1 & SocialSci  & 3135 & 350\\
USW30x & 2014T2 & SocialSci  & 8245 & 1045
\normalsize
\end{tabular}
\caption{
List of the $40$ MOOCs for which we trained automatic dropout detectors, along with their academic
field (social science, humanities, STEM, and health sciences), number of participants (registrants
who entered the MOOC courseware at least once), and number of certifiers. The total number of
\emph{unique} participants over all 40 MOOCs was $528349$.
}
\label{tbl:mooc_stats}
\end{table}

\subsection{Target labels}
\label{sec:targets}
The binary target labels used for training and evaluation were whether ($1$) or not ($0$) each student accrued enough points during the MOOC to earn a
certificate. The grade threshold for certification differed across the MOOCs but is typically around $70\%$.
Note that, starting in late 2015, some HarvardX MOOCs implemented a policy whereby only students who paid a fee to have their identity verified could
officially earn a certificate. For these
courses, we still considered the target label for a student to be $1$ as long as her/his point total exceeded the verification threshold -- in other words,
we ignored the fact of whether or not the student paid money to become ID-verified.

\subsection{Features}
\label{sec:features}
\begin{table}
\begin{tabular}{ll}\hline
\multicolumn{2}{c}{\bf List of person\_course features}\\\hline
LoE & YoB \\
gender & continent \\\hline
\multicolumn{2}{c}{\bf List of person\_course\_day features}\\\hline
avg\_dt &  sdv\_dt \\
max\_dt & n\_dt \\
sum\_dt &  nevents \\
nprogcheck &  nshow\_answer \\
nvideo & nproblem\_check \\
nforum &  ntranscript \\
nseq\_goto &  nseek\_video \\
npause\_video & nvideos\_viewed \\
nvideos\_watched\_sec &  nforum\_reads \\
nforum\_posts & nforum\_threads \\
nproblems\_answered & nproblems\_attempted \\
nproblems\_multiplechoice & nproblems\_choice \\
problems\_numerical & nproblems\_option \\
problems\_custom & nproblems\_string \\
problems\_mixed & nproblems\_formula \\
problems\_other & \\\hline
\multicolumn{2}{c}{\bf List of additional features}\\\hline
precourse\_survey & days\_since\_last\_action\\
\end{tabular}
\caption{Features extracted for MOOC dropout detection for the various training paradigms.
}
\label{tbl:features}
\end{table}

The features that were used for MOOC dropout prediction are
\emph{clickstream features} that are computed from the clickstream log that contains all interaction events between every student
and the MOOC courseware; this includes answers to quiz questions, play/pause/rewind events on  lecture videos, reading and writing
to the discussion form (the events, not the actual text), and more. The features we chose as the basis for automatic MOOC dropout
detection are similar to prior approaches (e.g., \cite{boyer2015transfer}) and can generalize to a wide variety
of MOOCs across academic disciplines; they  are
listed in Table \ref{tbl:features}.

\subsubsection{Feature extraction}
All features were extracted from two different
database tables  -- \begin{tt}person\_course\end{tt} and \begin{tt}person\_course\_day\end{tt} --
that are automatically updated daily by the \begin{tt}edx2bigquery\end{tt} data management framework \cite{ChuangLopez2016} that is used for MITx and HarvardX MOOCs.
The \begin{tt}person\_course\end{tt} features consist of the student's self-reported highest level of education (LoE), year of birth (YoB),
gender, and continent (Africa, Europe, etc.). Each of these variables was converted into a vector of binary dummy variables:
\begin{itemize}
\item Age: dummy variables based on the student's approximate age (computed as $2012 - \textrm{YoB}$) in the following ranges: $<10$, $10-15$, $15-20$, $20-25$, $25-30$, $30-35$,
$35-40$, $40-45$, $45-50$, $50-55$, $55-60$, $>60$ years, and null (no response).
\item LoE: elementary school, junior high school, high school, associate's degree, bachelor's degree, master's degree, professional degree, and null (no response).
\item Gender: male, female, other, and null (no response).
\item Continent: Europe, Oceania, Africa, Asia, Americas, North America, South America, and null (no response).
\end{itemize}

The \begin{tt}person\_course\_day\end{tt} dataset was used to compute clickstream features
such as the average and total amount of time spent in the course on a particular day (avg\_dt,
sum\_dt respectively), total number of clickstream events (nevents), etc.  Although \begin{tt}person\_course\_day\end{tt}
contains separate information for each student for each day of the course, we chose to aggregate information across time:
Specifically, when making a dropout prediction  for a specific student
at time $t$, we summed the values within each feature across all days $t' \leq t$. This approach
yields a feature representation that is both smaller and whose dimensionality does not change as a function of  time.\footnote{Note that, in pilot
experimentation we also tried using separate \begin{tt}person\_course\_day\end{tt} features for each day, but the accuracy was lower.}

Finally, we extracted two additional features: \begin{tt}precourse\_survey\end{tt},
which indicates whether or not the student responded to the course's pre-course survey (which might be positively correlated with commitment to the MOOC),
and the number of days since the student interacted with the MOOC courseware (which can be computed from data within \begin{tt}person\_course\_day\end{tt}).
In total (including all dummy variables and both demographic and tracking log features), there were $73$ features used for prediction.

\section{Training Paradigms}
We compared several different paradigms for training automatic MOOC dropout predictors:
\begin{enumerate}
\item \emph{Train on same course (post-hoc)}:
When predicting which students from course $c$ will drop out, train using features and target labels
from the exact same course $c$. Note that, since target labels for $c$ become available only \emph{after} $c$ has ended, this approach
essentially would require either that (a) the practitioner go ``back in time'' to when the MOOC first started,
or (b) that a new MOOC with the exact same distribution of students (demographics, prior knowledge, etc.) and with the exact
same content and structure is offered in the future, and that no exogenous factors (e.g., comedian Stephen Colbert talking
on television about MOOCs \cite{ho2014harvardx}) cause students to behave differently during the later incarnation of the course.
These assumptions are unlikely to hold. This approach is, however, a useful benchmark to compare against other training paradigms.
\item \emph{Train on other course from same field}: When predicting which students from course $c$ will drop out, train using features and target labels
from a different course $c'$ that has already completed, and for which the target labels  are thus already available. Although it is difficult to know
\emph{which} prior course should be used for training, a reasonable choice is a different course from within the same discipline (social sciences, humanities,
etc.). We chose to use the \emph{largest} such course in order to maximize the size of the dataset available for training.
\item \emph{Train on many other courses}: When predicting which students from course $c$ will drop out, train using features and target labels
from \emph{many} different courses (not necessarily within the same discipline). Specifically, for each course $c$, we trained a dropout classifier from each of
the $39$ other courses (recall that we tested $40$ MOOCs in total) and then averaged the classifiers' hyperplanes together.
\end{enumerate}

In addition, we also explore a novel training approach, similar to the \emph{in-situ} training method proposed
by \cite{boyer2015transfer}, based on using \emph{proxy labels}:
\begin{enumerate}
\item[4.]
\emph{Train using proxy labels (in-situ)}: When predicting at week $w$ which students from course $c$ will drop out,  train
using \emph{proxy labels} corresponding to whether each student \emph{persisted} -- i.e., interacted with the MOOC courseware at least once -- within the previous week $w-1$ (see Figure \ref{fig:insitu}).
\begin{figure}
\begin{center}
\includegraphics[width=3.3in]{Media/CertificationPredictionApproach2Schematic.pdf}
\end{center}
\caption{Schematic representing how to Train using proxy labels (in-situ): At each week $w$,
proxy labels of whether the persistent \emph{persisted} during week $w-1$ are used to predict
whether or not the student will certify/drop out by the end of the course.}
\label{fig:insitu}
\end{figure}
This approach can be implemented for any MOOC, and because it  does not require ``seeing into the future'' to obtain target labels,
it can deliver a dropout predictor that can be deployed during a \emph{live} MOOC, not just after it has finished.
There is, however, an inherent
mismatch between the \emph{training} target labels (based on persistence)
and the \emph{testing} target labels (based on certification/dropout), and this mismatch may degrade performance.
\end{enumerate}

\section{Classification architecture}
The classification architecture for the detectors was  logistic regression with $L_2$ regularization, which is equivalent to a 2-layer
neural network.
When estimating parameters of the logistic regression models, we varied the strength of the regularization parameter over the set $C \in \{ 10^{-4}, 10^{-3}, 10^{-2}, 10^{-1}, 10^0, 10^1, 10^2 \}$. Prediction accuracy on the test set did not substantially change (relative to the package-default value of $C=1$) over this set, and we thus report all experimental results for $C=1$.
We used the open-source \begin{tt}sklearn\end{tt} and \begin{tt}numpy\end{tt} packages for training.

\subsection{Baseline approaches}
In order to gauge how much ``added value'' is brought by machine learning approaches to MOOC dropout prediction that utilize
detailed clickstream information, we also compared the architectures described above to two simple baseline heuristics.
In particular, \emph{Baseline 1} uses only demographic information -- consisting of self-reported year of birth, continent of origin (Africa, North America, etc.),
level of education (primary/elementary school, high/secondary school, college, etc.), and gender -- to make predictions. These information
are available for each student as soon as she/he registers for the course; hence, no clickstream data is required. As with the other approaches,
logistic ridge regression was used for classifier training; we trained a separate logistic regression classifier for each course.

We also compared against an even simpler \emph{Baseline 2} which requires no machine learning
at all; rather, the predictor makes predictions based on the number of days since the student last interacted with the courseware.
In prior research on dropout detection \cite{WhitehillEtAl2015,KizilcecHalawa2015}, this variable alone has shown to be highly predictive of dropout.

\subsection{Normalization}
For {\em Train on same course (post-hoc)}, {\em Train on other course from same field}, and {\em Train on many other courses},
both the training and testing sets were normalized by subtracting each feature value by the feature-wise mean and dividing by the
feature-wise standard deviation, where the mean and standard deviation were calculated over only the training data. This normalization
is important to ensure that the L2 regularization affects all features similarly.
For {\em Train using proxy labels (in-situ)}, we used a different type of normalization to ensure that feature values are comparable
even when they are extracted across time windows of different lengths (see Figure 1). Specifically, we converted all clickstream
features from absolute counts to percentiles.

\subsection{Accuracy metric}
To measure accuracy of the dropout classifiers, we use the Area Under the receiver operating characteristics Curve (AUC) metric. The Receiver Operating Characteristics (ROC) curve itself plots the true positive rate versus the false positive rate of the trained classifier. The AUC is the integral of the ROC curve over the interval $[0,1]$ and answers the following question: Given one randomly chosen student who drops out from the MOOC and one randomly chosen student who certifies in the MOOC, what is the probability that the classifier can correctly distinguish the two students? A useless classifier that simply flips a coin will have an AUC of 0.5, whereas a perfect classifier will have an AUC of 1. (A classifier with AUC of 0 always makes the wrong discrimination.) Importantly, dropout accuracy as assessed by the AUC statistic is not affected by the overall proportion of students within a particular course who drop out (which is typically very high in MOOCs).

\subsection{When to Measure Accuracy}
In order to obtain reliable estimates of the accuracy of a MOOC dropout detector, it is important to consider over what time period the
accuracy is computed. For the goal of automated intervention, it is more useful to be able to predict early in the course, rather than later, which students
will eventually drop out.  Another point to consider is that, near the end of the MOOC, some students may have already accrued enough points
to earn a certificate. A dropout detector that predicts that such students will not drop out is not so much ``predicting'' these students'
future performance as it is reporting what they have already achieved; hence, accuracy statistics computed over such students may overestimate
the performance of the predictor. For both these reasons, we decided to compute accuracy over all weeks of each MOOC between the course launch
date ($T_{0\%}$, when instruction begins) and the earliest date by which students could possibly have earned enough points to earn a certificate ($T_{100\%}$).

Note that the earliest time at which predictions can be made differs across the training paradigms. Specifically, the \emph{Train on same course (post-hoc)}
approach can make predictions after just the first week of the course. In contrast, \emph{Train using proxy labels (in-situ)}
needs at least 2 weeks of clickstream data before the first set of proxy labels become available.  Simple baselines based on students' demographics that
require no clickstream data can be applied as soon as students register, which is a notable advantage compared to the machine learning-based
approaches.

\section{Results}
\begin{figure}
\begin{center}
\includegraphics[width=3.35in]{Media/aggregate_graph.pdf}
\end{center}
\caption{Results comparing different approaches.}
\label{fig:results}
\end{figure}
Dropout prediction accuracy (AUC) for all training approaches are shown in Figure
\ref{fig:results}. The horizontal axis indexes the week in the MOOC, where week 0 is defined for each course to be $T_{100\%}$. (Week $-3$
corresponds to 3 weeks prior to $T_{100\%}$, etc.) Each data point is shown with error bars corresponding to the standard error
of the mean. Since the lengths of the courses varied, the accuracy statistics for each week $w$ were computed using only those
MOOCs for which data were available at week $w$.

As shown in the graph, the most accurate prediction paradigm was \emph{Train on same course (post-hoc)},
which is the predominant training paradigm used in most prior dropout prediction research.  It
achieved an accuracy (averaged over all 8 weeks, and all MOOCs within each week) of $90.20\%$.
Perhaps more surprising is that the second most accurate approach was \emph{Train using proxy labels (in-situ)}. This approach
does not require any MOOC -- similar or dissimilar -- to have been offered previously. Despite the inherent mismatch between
the labels of persistence (did the student participate during the previous week?) and labels of certification (will the student
earn enough points to earn a certificate?), this approach attained an accuracy (averaged over all tested MOOCs and all 8 weeks) of $87.33\%$.

The third most accurate approach was \emph{Train on many other courses}, with an accuracy of $85.56\%$.
This training paradigm attained a higher accuracy than did \emph{Train on other course from same field} ($76.85\%$), suggesting that,
if it is not possible to exploit course-specific structure via either \emph{Train on same course (post-hoc)} or \emph{Train using proxy labels (in-situ)},
then it is better to harness prior data from a large variety of courses than from just a single course (even from within the same discipline).

\section{Comparison to Baseline Heuristics}
\emph{Baseline 1}, whose predictions are based solely on each student's self-reported demographics,
achieved an average prediction accuracy of $58.85\%$, suggesting that only a small amount of information about dropout is contained
in the demographics. (Note that we also tried a demographics-based classifier whose parameters were averaged over many courses, rather
than just for the one course itself; this approach's accuracy was even lower.)

\emph{Baseline 2}, whose predictions are solely on the number of days since the student's last interaction with the course, performed remarkably well:
It attained an average dropout prediction accuracy of $82.45\%$, which corroborates previous findings \cite{WhitehillEtAl2015,KizilcecHalawa2015}
that this variable is highly salient for prediction. Nonetheless, \emph{Baseline 2} was still substantially less accurate than either
\emph{Train on same course (post-hoc)} or \emph{Train using proxy labels (in-situ)}, suggesting that harnessing more detailed
clickstream features does bring a substantial accuracy boost.

\section{Accuracy across academic fields}
To explore whether dropout prediction accuracy tended to be higher for MOOCs within a particular academic field -- STEM, Humanities, Social
Sciences, or Health Sciences -- we implemented a linear mixed-effects model in which dropout prediction accuracy for the
\emph{Train on same course (post-hoc)} paradigm was estimated using
the week $w$ and field $f$ as fixed effects and the course
as a random effect.

{\bf Results}: The week $w$ was statistically significantly ($\chi^2(1)=184.77$, $p<0.0001$) correlated
with dropout prediction accuracy: for each week later in the course (i.e., closer to $T_{100\%}$), there was an estimated $1.53\%$ increase in prediction accuracy.
The difference in average accuracy varied only slightly as a function of the field: the prediction accuracy tended to be
slightly lower for STEM courses and slightly higher for humanities courses. However, the difference between the intercepts for these fields
was only $2.19\% - (-.16\%) = 2.35\%$; it was not statistically significant ($\chi^2(3)=1.9825$, $p=0.576$). This suggests that the features
listed in Table \ref{tbl:features} that we extract for dropout prediction are robust to changes in the MOOC subject matter.

\section{Deeper Prediction Architectures}
Most of the prior research on automatic MOOC dropout prediction has used generalized linear models -- including
logistic regression, support vector machines, and survival/hazard models -- to classify clickstream, natural language, and/or social
network features (see Table \ref{tbl:lit_review}). Only a handful \cite{fei2015temporal,ColemanEtAl2015,BalakrishnanBerkeley2013,xing2016temporal}
of approaches have considered
deeper learning architectures that can capitalize on nonlinear feature representations and interactions between features.
In this section, we explore the potential benefits of employing a deep, fully-connected, feed-forward neural network
for dropout prediction. Specifically, we performed an experiment in which we systematically vary both the depth
(number of hidden layers), as well as the width (number of neurons per hidden layer), of a neural network,
whose $73$ input features are the same as in Section \ref{sec:features}. Between the input and the first hidden layer,
and between each pair of consecutive hidden layers, are rectified linear units (ReLUs). The final, output layer
is a softmax layer.
The course we used for experimentation was the  MOOC with the greatest number of certifiers -- GSE2X (see Table \ref{tbl:mooc_stats}),
and we trained neural networks to predict dropout during the last week of the course prior to $T_{100\%}$.

To make experimentation more efficient, we utilized
a recently developed methodology for neural network training called Net2Net \cite{Net2Net2016}.
Net2Net is useful when training a sequence of  incrementally more complex networks.
In particular, with Net2Net there exists a \emph{teacher} network from which a new \emph{student} network
is built. The student network is deeper or wider than the teacher network, but the weights of the student network are initialized
prior to training either by (a) replicating neurons within a hidden layer and adjusting the weights to/from those neurons
to compensate (Net2Wider); or (b) initializing the weights of the neurons in an additional hidden layer to implement the identity function
(Net2Deeper). In this manner, the student network is guaranteed to produce identical outputs as the teacher network at the start
of the training and can become even more accurate during training.

\begin{figure}
\begin{center}
\includegraphics[width=3.35in]{Media/net}
\end{center}
\caption{Architecture of fully-connected feed-forward neural network for MOOC dropout detection. Each of the $h$ hidden layers
has $w^*=5$ neurons.}
\label{fig:net}
\end{figure}

Using Net2Net, we varied the number of hidden layers $h$ as well as the number of neurons per hidden layer $w$ -- see
Figure \ref{fig:net}. We began with $h=1,w=2$, i.e., a 3-layer neural network with 2 neurons in the hidden layer.
We then applied the Net2Wider technique using this network as the teacher and built a student network with
$w=3$ neurons  in the hidden layer. Next, the $w=3$ neuron network was used as the teacher,
and a network with $w=4$ neurons was constructed. This procedure was carried out iteratively until $w=15$ was reached,
such that a $w$-neuron student was trained from an $w-1$-neuron teacher, where $w \in \{3,4,\ldots,15\}$. The prediction accuracy
at each value of  $w$ was measured on test data; while the highest accuracy was attained for $w=6$, accuracy at
$w=5$ was very similar (and requires fewer parameters).
After maximizing accuracy on test data for $h=1$ with respect to $w$, we fixed $w^*=5$ and then applied the Net2Deeper approach
to maximize accuracy with respect to $h$. Specifically, we iteratively trained a student network with $h$ hidden layers from
a teacher hetwork with $h-1$ hidden layers, where $h\in\{2,3,\ldots,10\}$.

Each network was trained using stochastic gradient descent with no momentum, a learning rate of $0.1$,
over 20 epochs. The minibatch size was 10, and  the learning rate was annealed by a factor of $1/(1+10^{-3})$
after training on each mini-batch. To account for class imbalance, we tried
re-weighting the loss of the two classes; however, this technique did not increase test accuracy and was thus dropped. We used
the \begin{tt}keras\end{tt} software package for training.

\subsection{Results}
Results of maximizing both respect to $w$ and (separately, after fixing $w^*=5$) $h$ are shown in Figure \ref{fig:deep_net_results}.
For a network with $h=1$ hidden layer, the accuracy at $w=5$ was $97.43\%$. When keeping the width fixed at $w^*$,
the highest accuracy -- $97.55\%$ -- was achieved for $h=5$. For comparison,  logistic regression -- which is equivalent to
a neural network with $h=0$ hidden layers --  achieved an accuracy of $97.20\%$. The difference between these accuracies -- $97.55 - 97.20 = 0.35\%$ --
was statistically significant ($t(23738) = 78.47, p<0.00001$, two-tailed). For comparison, the difference in accuracies between the first-place
and second-place contestants in the 2015 KDD Cup contest on MOOC dropout prediction was $90.92 - 90.89= 0.03\%$ \cite{KDDCup2015}.
\begin{figure}
\begin{center}
\includegraphics[width=3.35in]{Media/N2Wider_AUC}\\
\includegraphics[width=3.35in]{Media/N2Deeper_AUC}
\end{center}
\caption{{\bf Top}: MOOC dropout prediction accuracy, on test data from GSE2x, of a neural network (with one hidden layer) as a function of the number of nodes in
the hidden layer. {\bf Bottom}: Accuracy of a neural network (with 5 nodes per hidden layer) as a function of the number of hidden layers.}
\label{fig:deep_net_results}
\end{figure}

{\bf Training cost}:
On GSE2x, whose training set contains $11870$ examples, and using the Net2Net training technique,
it took 80 seconds (an average of 4 seconds per epoch) for the best network ($w=5,h=5$) to train on an NVIDIA Tesla K20Xm GPU.

\section{Summary and conclusions}
In this paper we have investigated the \emph{practical} level of accuracy that can be achieved with an automatic MOOC
dropout predictor, given the fact that detectors must be trained before they can be deployed, using training data that are
collected \emph{before} their first deployment. In particular, we have compared several different training paradigms that are widely
used in the MOOC dropout prediction literature -- e.g., train on the same course (\emph{post-hoc}), train on different course
from same academic discipline, and train on many different courses -- on 40 different MOOCs that span a variety of
disciplines including the humanities, social sciences, health sciences, and STEM. Results suggest that the accuracy of classifiers
that are trained on data that are collected only \emph{after} a course has finished -- and which are thus unusable in the MOOC itself --
are often several percentage points higher than classifiers that are trained on other MOOCs. This underlines the importance of
careful accuracy estimation before conducting a large-scale intervention.

In addition, we have explored a training approach, similar to \emph{in-situ} training \cite{boyer2015transfer},
based on the idea of \emph{proxy labels} -- labels that approximate the quantity of interest (dropout versus certification)
but that can be collected \emph{before} a course has completed. Surprisingly, the accuracy of this approach is very similar to when
classifier are trained \emph{post-hoc} from data of courses that have already finished.
Moreover, this approach  enables a MOOC dropout classifier both to be trained and to be deployed while a course is ongoing.

Finally, we have presented evidence that deeper feature representations of basic demographic and clickstream features -- as implemented
with a deep (7 layers including 5 hidden layers with 5 neurons per hidden layer), fully-connected, feed-forward neural network can statistically
significantly increase the accuracy of MOOC dropout prediction relative to generalized linear models that
are the standard today. Using an iterative training strategy \cite{Net2Net2016},
the total training time to create such a network is modest and amenable to large-scale implementation over many (hundreds) of MOOCs
at regular training intervals (e.g., once per week).

{\bf Future work} on dropout detection should focus on moving from prediction to intervention. In particular,
more research should be invested in identifying not only those students who will drop out, but specifically those
who are likely to benefit most from interventions.

\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


text:
'CryptoPunk' NFT sells for $11.8M at Sotheby's
Jun 11, 2021 10:29 AM PHT
CRYPTOPUNK. A television camera is seen next to the non-fungible token (NFT) 'CryptoPunk #7523' a series of 10,000 unique pixel-art characters made by Larva Labs in 2017, during a media preview for the 'Natively Digital: A Curated NFT Sale' auction at Sotheby's in New York City, US, June 4, 2021
Shannon Stapleton/Reuters
Israeli entrepreneur Shalom Meckenzie buys the NFT for the sought-after pixel art character by Larva Labs
A non-fungible token (NFT) of a digital artwork called a "CryptoPunk" sold for $11.8 million on Thursday, June 10 auction house Sotheby's said.
CryptoPunks are a set of 10,000 pixel-art characters made by Larva Labs in 2017. The one sold by Sotheby's – "CryptoPunk #7523" – is of the sought-after Alien variety with blue-green skin, and wearing a medical mask.
The work is in the form of an NFT – meaning it is authenticated by blockchain, which certifies its originality and ownership. The NFT is sent to the buyer's cryptocurrency wallet; no physical artwork changes hands.
"CryptoPunk #7523" was sold for $11,754,000 as part of the Sotheby's online auction "Natively Digital: A Curated NFT Sale," which fetched a total of $17.1 million across works by 27 digital artists.
It was bought by the Israeli entrepreneur Shalom Meckenzie, who is the largest shareholder of digital sports company DraftKings, Sotheby's said.
Michael Bouhanna, Contemporary Art Specialist at Sotheby's said the sale demonstrated continued strong demand for NFTs.
"We are excited to continue to explore new and interesting ways in presenting these cutting-edge works."
Anyone can view the artworks the NFTs represent, but only the buyer has the official status of being the owner.
Sotheby's in the metaverse
Although the sale was on online auction, the works were displayed on screens in Sotheby's exhibitions in New York, London and Hong Kong.
Sotheby's also opened its first ever virtual gallery: a replica of its real-world building on New Bond Street in London, in the online virtual world Decentraland.
Decentraland is a blockchain-based virtual world, in which users can walk around with friends, visit buildings, go to events and display their NFT artwork collections.
NFTs explained: what they are, and why they're selling for millions of dollars
"To those who say, "yeah but you can't hang 'em on your wall", this is where virtual social worlds like Decentraland come in," said Dave Carr, a spokesman for the Decentraland Foundation.
"Virtual worlds are the natural home of digital art NFTs."
NFTs exploded in popularity in February and March. An NFT artwork fetched $69.3 million at Christie's, in the first sale by a major auction house of an artwork with no physical form.
Sotheby's first NFT auction was in April, with digital works by the artist known as "Pak" fetching $16.8 million.
"Quantum" by Kevin McCoy, a simple geometric animation also featured in Thursday's auction, sold for $1.47 million. Sotheby's said it was the first NFT ever made, having been "minted" in 2014. A work by Pak was also sold for $528,200. – Rappler.com
Apple hires Meta's comms head for augmented reality – Bloomberg
1,000-fold increase in computing power needed for hyper-visual, connected metaverse – Intel
LISTEN: The Philippine Digital Convention 2021 podcast
[Science Solitaire] Homo obliviscius (common name: Tragically forgetful human)
More on Emerging technologies

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text:
Steve Nash, Steve Blake near returns for Lakers
SportsNBALakers
Lakers' Pau Gasol, Steve Nash, and Steve Blake during game four of their NBA Western Conference playoffs at the Staples Center Saturday, April 28, 2013. The Spurs beat the Lakers 120-89. The Spurs beat the Lakers 103-82. (Hans Gutknecht/Staff Photographer)
PUBLISHED: January 29, 2014 at 8:54 p.m. | UPDATED: August 28, 2017 at 7:58 a.m.
Lost in the wreckage of Indiana's 104-92 victory Tuesday night, which included the news that Kobe Bryant is still three weeks away from his next doctor's evaluation, was a glimmer of hope.
Soon, the Lakers will start getting point guards back. Steve Nash is expected to step onto the practice court Thursday and Steve Blake's elbow ligament has healed enough that he has been cleared to step onto the court for limited time, as has Jordan Farmar with his left hamstring tear.
Forward Xavier Henry (bone bruise) had filled in some at point guard, though his return is expected later down the road.
When any and all return, it will provide some relief for Kendall Marshall, who has proved to be much more than a stopgap measure. As the team's only point guard, he has logged almost 40 minutes a game over 14 games.
On the other hand, in the last 12 games Marshall has averaged 12 points and 11.5 assists per game. So when everyone is healthy, who's going to get the playing time?
"I take more pride in winning," Marshall said after the game.
Lakers coach Mike D'Antoni has not let negativity seep into the locker room, and he hasn't detected any, either. His advice through the trying period has been simple.
"Just play hard," D'Antoni said. "We'll get some guys back, the Steves and Farmar should be back pretty soon, and hopefully X later."
Pleading the third
It happened again against the Pacers. The Lakers turned in a solid first half, playing to a 49-49 tie heading into halftime, when suddenly they shot 28 percent in the third quarter and the Pacers built a 10-point lead over the next 12 minutes, eventually extending their advantage to as many as 16 points.
The third-quarter debacles have been well chronicled but may never be solved.
"We just didn't play very well, but it's not a starting lineup (issue)," D'Antoni said. "We've tried that. We've tried different people, we've tried different things. We've tried getting them out earlier to warm up, talk to them, encourage them, but for whatever reason we just don't seem to get off the schneid very well to start the second half.
"I don't know what kind of adjustments we need to make, but we just need to play better."
The Lakers did have a different lineup than their starters for the third quarter but not by choice. Pau Gasol's groin tightened up and he couldn't enter until 10:41 left in the period. He will be re-evaluated today.
The Pacers (a season-high 99) and Lakers (94) combined for 193 shots and both teams missed 57 shots. That left a lot of rebounds, and the Pacers left with a 63-50 advantage, and they had 20 second-chance points.
"It has to be a team effort," Gasol said of the Lakers' rebounding. "Everyone has to come into the paint, get aggressive when there is so much penetration. You have to crack back on the big guys, feel them and don't allow them to get to the board. Little things like that can make a big difference. We just have to scramble more."
Lakers' Russell Westbrook scores 35 but misses final shot in loss to Hornets
Jimmy Butler, Heat snuff out Clippers' latest comeback bid


The Daily Reckoning Presents: A Guest Essay in which Edward Chancellor, author of "Devil Take the Hindmost", considers a recent proposal for legislation in the U.S. to temporarily ban short-selling.
The professional life of the bear speculator is normally short and full of misery.
Powerful forces are aligned against the bear. They include, the bull speculators, whose capital outnumbers that of the bears by at least one hundred to one; the public, who perceive short-selling as an injurious activity; and politicians, who are always eager to blame the short-sellers for economic woes.
In addition, the bear pits himself against the forces of economic progress, which over the past two hundred years have been accompanied by the most tremendous gains in share prices, so that $100 invested in the U.S. market in 1802 would have been worth around $700m by the new millennium.
Bears have always been unpopular. In 1609, Flemish-born merchant, Isaac Le Maire, organized a bear raid on the stock of the Dutch East India Company [even though a founding member of the company]. Although the Amsterdam bourse maintained that the decline in the East India stock was due to poor business conditions – not short- selling – in 1610 the government outlawed all short sales. As with most laws seeking to curtail the activities of bears – the market's natural libertarians – this edict was a dead letter from the start. The Dutch banned short-selling again in 1621 but to no effect.
As the stock markets became established in Britain and France in the eighteenth century, further legislative traps were laid to catch the bears. Following the collapse of the Mississippi bubble in 1720, bears, who had profited from the decline in the Mississippi stock, were fined and a law was introduced outlawing short- sales.
Sir John Barnard's Act, as it was called, outlawed the use of futures, options, and short sales of stock (by an error of drafting, this was later understood by the courts to relate only to British government stocks).
It remained on the statute book until 1860. From its beginning, however, few paid attention to the letter of the law: brokers continued to engage in short sales which were enforced through a gentlemanly code of conduct – "my word is my bond" – rather than legal sanction.
These two early pieces of legislation against short- selling reveal a common theme in the history of the bears. Bubbles occur when speculators drive asset prices far above their intrinsic value. The collapse of a bubble is frequently accompanied by an economic crisis. Who gets the blame for this crisis? Not the bulls, who were responsible for the bubble and the various frauds and manipulations perpetrated to keep shares high, while cashing in their profits.
No, it is invariably the bears who are blamed for the post-bubble crises and are the main objects of anti- speculative legislation. Yet during the bubble periods it is the bears who are generally the lone voice of reason, warning people of the folly of investing in overpriced markets. In the aftermath of a bubble, they continue their forensic work of exposing unsound securities and bringing prices back in line with intrinsic values, a point which must be reached before the recovery can start.
Only the other day, following the fall of the markets after September 11, the Belgian finance minister said he had "strong suspicions" that the UK markets were used for speculative trading!
Bears have always operated more freely in the United States than in Europe. Despite a ban on short sales by the New York Legislature in 1812, the bear operator was a familiar figure in the nineteenth century. A few gained celebrity. Jacob Little, a saturnine figure, was a leading bear operator in the first half of the century. Known variously as the "Great Bear," the "Old Bear," and the "Napoleon of Wall Street", Little also operated on the long side, and perfected the technique of catching shorts in corners, which became a characteristic feature of the U.S. market. Little was destroyed in the "Western Blizzard" crash of 1857.
His place was taken by Daniel Drew, also known as the "Great Bear", "Ursa Major", and the "Sphinx of Wall Street". Drew was described by a contemporary as "shrewd, unscrupulous, and very illiterate – a strange combination of superstition and faithlessness, of daring and timidity – often good-natured and sometimes generous." He was the great rival of Cornelius Vanderbilt and a sometime partner of Jay Gould.
The roaring twenties, of course, belonged to the bulls. But as the market turned in September 1929, the bears regained control. The celebrated speculator Jesse Livermore, who as a teenager made his first fortune shorting the stock of the Union Pacific Railroad during the San Francisco earthquake of 1906, made another pile during the October crash.
[After the crash] stocks continued to fall, until by the summer of 1932, the Dow Jones reached a floor of 41.88, nearly 90% off its 1929 peak. By this date, the country's national income had shrunk by 60% and one third of the non-agricultural workforce was unemployed. President Herbert Hoover, who came to office in early 1929 promising that "the end of poverty was in sight," faced an uphill task in the forthcoming election. America needed a scapegoat.
Wild rumors spread of bear raids, of fabulous profits made by short-sellers, and of political conspiracies hatched by foreigners interested in bringing down the market, the dollar and the U.S. economy. In early 1932, the Philadelphia Public Ledger maintained that "European capitalists had supplied much of the cash needed to engineer the greatest bear raid in history. These proverbially open-handed and trusting gentleman had accepted the leadership of New York's adroit Democratic financier, Bernard Baruch." Baruch, the best known short-seller in the country, shrugged off the charge.
Hoover, on the other hand, apparently became convinced that bear raids on the stock market were intended to damage his presidency. In April 1932, a French stock market rag was raided by Paris police, its female editor accused of being in the pay of Russian and German interests who were trying to induce a panic on the New York market. In desperation, Hoover ordered the Senate to open an investigation into the affairs of Wall Street.
In fact, there is remarkably little evidence of organized bear raiding on the U.S. market following the October Crash. In order to dispel the myths, the economist of the New York Stock Exchange, Edward Meeker, published a book, entitled Short-Selling, in 1932. Meeker claimed that bears had not precipitated the crash. In November 1929, the NYSE found that around one hundredth of one percent of outstanding shares had been sold short. A later study in May 1931 found the short interest had risen to 3/5 of one percent of the total market value. More than ten times as many shares were held on margin. Nor could the stock exchange identify any bear raids in the subsequent market decline.
Meeker provided an eloquent defense of short sales. He argued that the bears stabilize prices by providing liquidity and creating demand – by covering their shorts – in a falling market. Shorting was not illegitimate, in his view. "A short sale," wrote Meeker, "represents a debt contracted in goods rather than money." In this it was similar to many other business contracts.
It is unlikely that many were swayed by Meeker's argument. The politicians certainly were not. However, the Senate investigation into Wall Street, intended to uncover the nefarious activities of the shorts, found little to go on. A list of 350 leading bear speculators presented to the committee contained only one familiar name…Having no luck with the bears, the investigation turned its attention to the bulls of yesteryear. This was much more fertile ground.
The Pecora hearings, as they became known (after their lead counsel, Ferdinand Pecora), revealed the seamier side of Wall Street during the bull market: the involvement of leading firms and bankers in the manipulation of share prices, the dumping of unseasoned securities on an innocent public, the fleecing of the firms' own clients, the preferential distribution of shares to favored friends, and so on.
In other words, rather similar behavior to what we have witnessed from the investment banks in recent years.These findings led to the New Deal legislation of 1933 and 1934, which involved among other things, thecreation of the Securities and Exchange Commission andthe separation of commercial and investment banking.
The pattern of boom and bust has continued in the post- war years. Inevitably the bears have been blamed during every major downturn…Japanese authorities complain[ed] that mysterious foreign interests were responsible for the decline in their stock market, following the great boom of the bubble economy. (In 1998, the Japanese imposed restrictions on short-selling in an attempt to shore up their market).
In every instance when bears are accused of bringing down a market, we find that it was the preceding bull market, with its accompanying misallocation of resources and unsustainable accumulation of debt, which was the root cause of the decline.
Today is no different. Earlier this year, as markets declined, there were isolated complaints of bear raids. After September 11 these complaints assumed a more hysterical tone. It was alleged that terrorists had arranged to short airline and insurance stocks prior to the attack on the United States. I do not know whether this is true.
However, I am doubtful. As we have seen in the past, foreigners have frequently been identified as leading a conspiracy of bear raiders. And besides, there were good fundamental reasons to short airlines and insurance companies even before their position deteriorated in September.
Nevertheless, last month lawmakers in the U.S. asked the SEC to consider a temporary ban on short trading. According to newspaper reports, UBS Warburg and Bear Stearns tried to limit short sales by their clients.
It has been said that "progress in finance is cyclical rather than linear." Certainly attitudes towards short- selling seem to have progressed very little over the centuries.
Bulls always have been more popular in this country because optimism is so strong a part of our heritage. Still, over-optimism is capable of doing more damage than pessimism since caution tends to be thrown aside.
To enjoy the advantages of a free market, one must have both buyers and sellers, both bulls and bears. A market without bears would be like a nation without a free press. There would be no one to criticize and restrain the false optimism that always leads to disaster.
*** As if they were acting with "total clarity" in the first place, the anti-globalization movement has been "stunned" by the attacks on New York and Washington, reports the International Herald Tribune. What a pity… the Battle of Seattle was nothing if not entertaining. And the Daily Reckoning was agin' to like dem folks.
*** Those pesky terrorists are spoiling everybody's fun.
*** A separate report released Thursday by the Commerce Department showed manufacturing activity dropping to its lowest level in 11 years.
*** Prices of the long-term bonds soared on the announcement…yields plunged. "It was the biggest single-day move since investors fled to safety during the stock market crash of 1987…keeping consumers feeling flush has become a top priority." Perhaps even a matter of national security.
*** "This past summer," Myers offers by way of example, "we recommended purchasing December 2002 $4.50 Comex silver calls for 30 cents, or $1,500 each. We recommended selling them a week after the World Trade Center attack when they were going for 53.2 cents, or $2,660 each." A tidy profit, indeed.


text:
Q: Gradle file push to github repository I was pushing my Android application project to my github repository. I saw two folder .gradle and gradle were not pushed but another folder called gradle/wrapper instead got pushed. I am not sure where this gradle/wrapper folder came from and if this is a normal thing that happens when pushing an Android app.
Edit: I closely inspected a bit more and the gradle folder that I was talking about is the gradle/wrapper folder. It is just that I didn't expand that folder initially.  The .gradle folder is still not showing up in my Github repository though.

A: The .gradle folder should not be committed and should be included as part of the gitignore
https://www.gitignore.io/api/android,androidstudio
This is the directory used by the wrapper on your local machine to store the downloaded Gradle dependencies (as compared to installing Gradle separately yourself and configuring the IDE to use that)
build.gradle and settings.gradle should be the least amount of required files for any Gradle project, the wrapper is optional but recommended to standardize the version of Gradle used to build the project

A: if you don't see a file that you have pushed via git, please check your .gitignore file.
Gradle/wrapper folder is generated upon building.

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Offbeat, unusual, unconventional & interesting career interviews
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Business, Design, Excludes Science/Medical, Furniture, Interior
Interior Designer Interview
Date: January 29, 2018Author: The Interview Portal 0 Comments
Please tell us about yourself
At 33, she has designed some of the most talked about spaces in the city.
A finance and marketing graduate who loved math, Shruti Jaipuria always had a fiery creative streak, something she refused to sideline. Today, having earned her chops at the prestigious Parsons School of Design, New York, the 33-year-old Bengalurean is a high-flier in the interior design space.
https://www.deccanchronicle.com/lifestyle/viral-and-trending/280516/summer-of-soul-stirs.html
Her vision is now the soul of some of Bengaluru's beloved restaurants, lounges, art galleries, retail boutiques and residential projects. What can hard work and commitment do? This is that story.
How did you end up in such an offbeat, unconventional and cool career?
"Growing up, I was always drawn to the creative, but I loved math as well," she tells us. She continued to teach herself software and use it for fun on the side. "I also took as many drawings classes as I could," she says. She quit her job as a consultant and enrolled in Parsons School of Design – The New School (Interior/Spatial Design). She then went on to train at Tony Chi & Associates at NYC and local firms in Bangalore. and founded Maia Design. And then, there was no looking back.
Tell us about your work
Drawing inspiration from the world around her and constantly questioning how it can be applied to the world of contemporary is something this creative director dwells in. "We have a lot of tools at our office – blocks, magnets, play dough, different materials that we just like to play with. It's like being a child again!
Curiosity, fun and wonderment are at the core of the creative process for us," she explains, working at her home away from home, her office of five years. They work with artists, often defining their 'experiences' with installations.
A few interiors that you have done?
A talk show set, the plush interiors of Toast & Tonic, the quirky dining space at The Black Rabbit, the warm and earthiness of Arbor Brewing Company, the incubation labs at Titan, designer Namrata Joshipura's store at the Leela are just a few that remind us city folk of what Shruti has been up to. The latest feather in her cap? The Black Rabbit's interiors were featured as one of the best in India by a popular design magazine!
Challenges are imminent but the easy going lass seems to be unfazed by it. "It's a very, very disorganised industry and you need to work with people who are not very professional.
My greatest learning has been patience," she dishes out, as we catch her at her office on a busy day. She finds time for herself too, "I am a pretty easygoing person.
I love to eat, work out, spend time with my dog, cook a little, travel and I'm getting more and more involved and intrigued by classical yoga." What's next? A microbrewery, a spatial brand identity and store/lounge concept for a chocolate brand, a coffee roastery, an organic store, a few residential projects, and a holiday home on the hills is all on her plate. But she still has this to say, "I have a long way to go."
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Children's fashion gets a second chance at Kids O'Clock
Laura Vidrequin, founder of the Kids O'Clock secondhand site for children, shares the success story of a female entrepreneur.
Would you explain Kids O'Clock for us? What prompted you to create the project? How did it start? What's your background?
Kids O'Clock is a marketplace dedicated to buying and selling secondhand items for children from 0 to 14 years old. In the beginning we concentrated on clothing, and then we expanded into games, strollers, car seats, etc.
Before starting the project, I had worked as a buyer for 10 years, so I was always involved in analyzing retail, collections and products. Then three years ago, I had my little boy, and I realized that in the children's market there was no trustworthy platform helping to educate the public and integrate the secondhand economy and circularity. So I decided to tackle the problem.
There was nothing like that on the market? Nobody had already thought of it?
It's not so much that nobody had thought of it. Neighbors and friends have given clothing back and forth since the beginning of time. But it's the way we do it that makes a difference.
Today when you have a new baby you feel very alone. There are many reasons for that: families break apart, children leave … so you need to be reassured. It was important to me to create a platform that could be trusted, with a brand image I could identify with. I always thought there were a lot of stale conventions in children's fashion: blue, pink and codes that could sometimes be slightly out of date. Kids O'Clock stands out from the other players – our biggest competitors are Vinted and the Facebook marketplace.
Laura Vidrequin
What makes Kids O'Clock original? What is unique about you?
Our team! We're all women, and our different strengths and professional backgrounds have let us create a very strong brand image. So much so that we're launching the Kids O'Clock for Business white label, which is a turnkey service helping brands be more circular. Our role is to simplify the ongoing environmental transition and give brands support in accomplishing that transformation. We're already working with a few brands, and we're eager to see the project move forward and grow.
Did the pandemic accelerate new digital practices and expand CSR awareness?
Yes, enormously! American Vogue wrote about us in a 5 May 2020 article, and our platform wasn't even finished. The journalist was very optimistic, telling us that this was the future and the new way to consume. And it's true that with all the circular and green trends, Kids O'Clock really showed up at the right time!
When I started Kids O'Clock I was still very immersed in buying and always on a plane, but all that stopped abruptly in February 2020. I had already thought about the project, so I was able to bounce back and put all my energy into this new adventure.
The secondhand market is going strong. Why do you think that is?
There's a CSR and anti-waste movement, but I also think that, above all, there's a deep need to use what already exists. It started 15 years ago with leather goods and shoes from the big luxury names as well as with furniture and even electronics.
In the 1980s our parents bought their televisions, cat food and paper towels in the same place. Then the same thing happened with Amazon; there were no specific profiles in ecommerce, you could buy everything on the same site.
But consumer trends change: now we're opening specialty shops for everything, like shops only for bike parts or marketplaces specializing in secondhand goods for children! People need to be able to trust businesses and to turn to the professionals in each sector.
@kids_oclock
Consuming more responsibly is great, but shouldn't we also pay attention to the digital pollution, overpackaging and transportation this activity generates?
It's very difficult to measure all these impacts. We're applying for B Corporation Certification, so, of course, these are issues we pay attention to.
To reduce transportation, we're trying to open depots where there's the most activity. We have one in France and one in England, and we plan on opening a third in the United States in an effort to manage our carbon footprint.
In addition, we use recycled paper for packaging, and we don't dry clean the clothes we receive. If the clothes we get can't be used, they are immediately given to charity.
What's the next step? Do you have new projects lined up?
We have several! We introduced an application in January, and we're still fine-tuning that. Then there's Kids O'Clock Business, which will be up and running very soon. And this spring we'll be working on new, more responsible ways to consume and on partnerships and other projects. I won't tell you any more right now, so stay tuned!
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I speak from experience: a few months ago, clear out of nowhere, I was struck by a need for homemade waffles. I had to have them—and because I live in New York City and expect immediate gratification, I proceeded directly to my local discount emporium and procured a plastic waffle iron—covered in a thin film of dust—for the sum of $16.99. Back home, I washed it and hastily set about mixing up a bowl of batter. Sadly, this story has a grim ending: even after the the requisite heating and greasing, pouring and griddling, the specimens that emerged from my iron were flabby and pale—hardly deserving of the name waffle and certainly a far cry from the feast of my dreamings.
One of the most important attributes of a waffle maker is how well its nonstick coating works—there's nothing worse than trying to clean stuck-on waffle from those narrow cracks. Luckily, waffles popped out easily from the Krups GQ502D with the aid of silicone tongs or chopsticks, even on the one or two occasions when opening the lid took a little prying. The manual recommends oiling the plates just once each time you use the machine, and we found that this step was more than enough to keep waffles from sticking, even through many rounds of batter. Best of all, the waffle plates detach from the machine, so once they're cool you can pop them in the sink and wash them with soap and water, or, according to the manufacturer, even run them through the dishwasher. This is so much easier than cleaning most of the other machines we tried, including our former top pick, which requires you to wipe down the plates still in the machine with first a soapy cloth and then a damp one; inevitably, some soap seems to cling stubbornly in the cracks. But only two other machines we tested—the Nordic Ware stovetop model and Black+Decker's brand-new (as of 2016) Removable Plate Waffle Maker, model WM700R—featured removable plates, and both fell far short of the Krups in ease of use and quality of waffles.
Although stovetop waffle makers are a little harder to use than electric because you have to regulate the waffle iron's temperature and cooking time, they're also more versatile. They can be used for tailgating, camping trips, or during a power outage (if you have a gas stove). Stovetop waffle irons are usually much smaller and thinner than countertop models, too; so they're the ideal choice for small kitchens with limited storage, camping or tailgating, off-the-grid living, or anyone who enjoys the challenge of learning to create the perfect waffle by hand.
Hungry diners know whether or not their breakfast is ready when the Cuisinart's convenient "ready to bake/ready to eat" indicator lights with corresponding red and green hues come on. The Cuisinart features a cool-touch lid and round, non-stick cooking plates with four quarters, giving users the option of having a single waffle or dividing it up. We particularly like how this waffle maker, in its brushed stainless steel housing, stands on end for convenient storage in tight spaces.
The thermostat allows the automatic control of the temperature in the waffle iron. However, the "Twin-O-Matic" also has a thermometer -- a device that indicates the actual temperature of the iron. The principle behing the thermomenter is basically the same - a bimetallic strip is made with the property that its warping is a linear function of temperature. This strip is connected to a lever mechanism that causes the small needle to rotate. Hence, the position of the needle is a direct function of the temperature. The dial is calibrated to reflect this.


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Products – Alistair Hulett
Remembering the Life and Legacy of Alistair Hulett
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The Malkies, Suited and Booted Download1 x £0.00
View basket "The Malkies, Suited and Booted Download" has been added to your basket.
The Complete Works of Roaring Jack CD
During the four years these recordings were made, Roaring Jack was involved in more than a few political campaigns. Some, like the fight to overturn the Labour Government's de-registration of the Builders Labourers Federation (BLF) and the workers' occupation of the repair yard on Cockatoo island, ended in defeat.
Red Clydeside CD
Featuring Alistair Hulett (vocals, guitar), Dave Swarbrick (fiddles, viola, baritone violin) and Kevin Dempsey (second guitair on 'The Granite Cage'). All songs written by Alistair Hulett, arrangements by Alistair Hulett and Dave Swarbrick. The songs on this disc were written over a period of two years.
Dance of the Underclass CD
His first solo CD and one of our favourite Alistair Hullett recordings. An acoustic, but fiery, album which brought him back into the folk field.
Alistair Hulett Live in Concert CD
When Alistair talked about recording a live concert, he said he'd never been successful in getting a good live recording, and wished us luck. Well it worked, and we have a wonderful snapshot of Alistair Hulett live in concert, complete with introductions, blue notes, patter, chatter, laughter, wisdom, glass clinks, chair scrapes, chuckles and applause
Victor Jara T-Shirt
This T-Shirt was produced for the 40th anniversary of the murder of Victor Jara in the coup in Chile which overthrew the democratically elected socialist government of Salvador Allende. All money raised from the purchase of this T-Shirt will be divided between the Justice for Victor Jara campaign and the Alistair Hulett Memorial Trust.
The Complete Works of Roaring Jack Download
This is a Downloadable product. Upon purchasing you will be prompted to download a zip file containing the track artwork and MP3 files included in this album. These are DRM free tracks, however once purchased they can only be downloaded from the Alistair Hulett Store a maximum of five times, and you must download within 14 days, so ensure you backup your purchases once downloaded. Album Information: Includes all tracks listed below, 164.8 MB size, 44100 Hz sample rate, MP3 format. Artwork Information: 700 x 700 pixels.
Justice for Victor Jara CD
This CD was produced for the 40th anniversary of the murder of Victor Jara in the coup in Chile which overthrew the democratically elected socialist government of Salvador Allende.
In the Back Streets of Paradise CD
Featuring Alistair Hulett (vocals, guitar), Jimmy Gregory (bouzouki, guitars, tin whistle), John Deery (Uilleann Pipes), Phil Murray (accordion), Janek Bagoien (violin, harmonica), Anthony Ryan (bass), Brian O'Kelly (bodhran) and Rod Gilchrist (snare drum). In the Back Streets of Paradise was produced by Craig Beck, Jimmy Gregory and Alistair Hulett, it was recorded and mixed by Craig Beck at Power House Studios, Sydney in 1994.
Red Clydeside Download
This is a Downloadable product. Upon purchasing you will be prompted to download a zip file containing the track artwork and MP3 files included in this album. These are DRM free tracks, however once purchased they can only be downloaded from the Alistair Hulett Store a maximum of five times, and you must download within 14 days, so ensure you backup your purchases once downloaded. Album Information: Includes all tracks listed below, 58 MB size, 44100 Hz sample rate, MP3 format, Artwork Information: 600 x 600 pixels.
Red Clydeside T-Shirt
This Red Clydeside Design was produced for the 2013 Alistair Hulett Memorial Concert in the UK, they are 100% cotton and machine washable. This T-Shirt is available in small, medium, large, extra large and extra extra large, don't forget to select which size you want from the drop down list above! The T-Shirt reads "I am not here as the accused: I am here as the accuser of Capitalism dripping with blood from head to foot". All proceeds from the sale of this T-Shirt will go to the Alistair Hulett Memorial Trust in the UK and the Alistair Hulett Memorial Fund in Australia.
Love, Loss and Liberty CD
Love, Loss and Liberty is the first in a series of compilation CDs covering the Songs of Alistair Hulett. It represents a small insight into the diversity and breadth of the wide-ranging material that Alistair created over 35 years as a songwriter. It demonstrates his artistry and eloquence as a poet and the impact he had on musicians and artists across the musical spectrum.
The Malkies, Suited and Booted CD
Featuring Hugh Whitaker (drums, percussion, backing vocals), Rachel Goodwin (vocals), Alistair Hulett (vocals, guitar), Hugh Bradley (bass, backing vocals), Gavin Livingstone (backing vocals) and Phil Snell (mandolin, fiddle, lap steel, guitar).
Remembering Alistair Hulett
Alistair Hulett was a singer, songwriter, international socialist and revolutionary. He was a kind, gentle man who was committed to fighting for a better world, a world based on the principles of justice, equality, love and respect for all of humanity. The world was a better place for knowing him and is a sadder place for his loss. He left a great legacy in his music that will continue to bring inspiration to many who, like him, believe a better world was possible.
This website is dedicated to Alistair Hulett and is maintained by the Alistair Hulett Memorial Trust.
The site proudly continues the legacy in the form of his writing, recordings and the widespread influence he had through his activism on behalf of many social justice causes.
The mission of the Alistair Hulett Memorial Trust (UK), and the Alistair Hulett Memorial Fund (AU) is to help to promote social justice and equality through music and struggle.
Unless stated otherwise, the copyright in this website and the material on this website (including without limitation the text, computer code, artwork, photographs, images, music, audio material, video material and audio-visual material on this website) is owned by the Alistair Hulett Memorial Trust and the Alistair Hulett Memorial Fund ©. All rights reserved.
Unless stated, all content on this website is owned by the Alistair Hulett Memorial Trust and the Alistair Hulett Memorial Fund ©. All rights reserved.
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You may remember these two from their Piedmont Park Engagement Shoot this spring! They are truly some of the most kind hearted people you will ever meet and are so fun and easy-going! We were blessed with such a perfect day for their Biltmore Ballrooms Wedding and I can't say enough the joy it was to spend it not only with them, but so many of their loved ones. The kindness that is present in these families is unparalleled and just continues to remind me of why I love my job so much. Because not only am I blessed with the honor of coming alongside a couple for one of the biggest days of their lives, but I get to learn so much about love, life, and family in the process than I could ever contain in a blog post and that is priceless! Thank you guys for having me be a part of such an incredible day and for shining such a light of unconditional love not only with each other, but everyone in your life. It is breath-taking!


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The Death of Town Hall Meetings in America
By Staff on March 14, 2017 13 Comments
Congressional Republicans have unsurprisingly received criticism for calling out leftist protesters who are part of a nationally coordinated campaign to hijack their town hall meetings. These forums have become such a media spectacle that Senator Marco Rubio decided to forgo town halls all together.
This has raised the question of whether or not the town hall meeting is indeed dead in America.
Norman Rockwell's 1941 print, Freedom of Speech is my favorite representation of a town hall meeting. Hanging prominently on my office wall, I'm inspired by the artwork depicting a lone dissenter, Jim Edgerton, standing up in a town hall meeting in opposition to a proposal for a new school.
Rockwell illustrates Mr. Edgerton, and his minority point of view, with Lincolnesque dignity, symbolizing how we, the people, drive democracy and how democracy empowers all of its people. It captures the greatness of our nation in a New England town hall meeting during the Great Depression.
However, the good old days of democratic discourse at town hall meetings idealized by Rockwell are well behind us. I would argue that town halls no longer serve as a stage for genuine voices to be heard, ideas to be exchanged, and everyday citizens to participate in our political processes.
Instead, town hall meetings have been overtaken by leftist professional protesters who are either paid or scripted to disrupt, distract and in many cases destroy any meaningful dialogue on public policy.
Recently, Senator Marco Rubio received a lot of media criticism for not holding town hall meetings. In a recent interview with a Miami news outlet he said, "They are not town halls anymore. What these groups really want is for me to schedule a public forum, they then organize three, four, five, six hundred liberal activists…heckling and screaming at me in front of cameras."
Senator Rubio is right and he correctly identified the X-factor that has led to the death of town hall meetings in America.
This X-factor is very simple to see and understand: Protesters do not exist without the news media and the news media needs the protesters for conflict, which is why both are so critical of republicans like Senator Rubio, who've opted out of the town hall forum.
The news media expects professional protesters to hijack town hall meetings enabling reporters to repeat the well-played narrative of angry citizens mobilizing to voice their concerns.
Unlike the organic uprising of the Tea Party movement, the news media must assist liberal groups to propagate the Jerry Springer narrative showcasing republicans under fire and out of touch with their so-called real constituents.
In reality, these conflicts are leftist illusions manufactured by DC-centric professional protesters who are paid to coach, direct and mobilize local activists to torpedo the forum in order to advance their political agenda of divisiveness.
Recent town halls meetings sponsored by Congressmen Gus Bilirakis from the Tampa Bay area and Matt Gaetz from the Florida Panhandle were hijacked by these leftist professional protesters generating front page, headline, breaking news.
By comparison, Democratic Congressman Darren Soto of the Orlando area had a very quiet town hall meeting. His forum turned into a mutual admiration society that was so civil that the Congressman had to prompt the audience with questions to see if there was anyone in opposition. As you can guess, there wasn't as much media coverage on his town hall.
Sadly, we can no longer expect to hear the voice of Jim Edgerton standing up for his unscripted beliefs at a town hall meeting. This once-popular public platform has been terminally dismantled by leftist activist groups who are adept at exploiting public fears and anger. Town hall meetings have become outdated and ineffective as a useful means of educating and connecting with the public. They're nothing more than an activist vehicle that fuels anxieties and manufactures illusions via the news and social media.
Republicans who have endured these Astroturf protests are to be commended. However, they would be better served by empowering constituents with public forums such as Twitter, tele-town halls or membership groups that preclude the tactics of leftist protesters who are hell-bent on undermining our democratic way of life.
Patrick Slevin is a former Florida mayor who leads his global consulting practice SL7 Consulting, headquartered in Tallahassee, Florida. Go to www.PatrickSlevin.com for more information about his firm.
Pat Slevin
The Death of Town Hall Meetings in America added by Staff on March 14, 2017
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13 Responses to "The Death of Town Hall Meetings in America"
Ireadyou says:
I've never been a fan of these "meetings". When they were under control, the speaker talked most of the meeting time, delivering a slanted message design to lead the audience somewhere while being short of verifiable facts. Q&A was relatively short, sometimes the Questions had to be screened to see if that one would be allowed. The answers usually were a reiteration of the talking points delivered in the speech before Q&A.
And then there are always the angry guys who rage on from the floor microphone. IF people had to pay 25 cents to go to one they'd all realize how a waste of money they are. And that was before the people turned into raging lunatics.
Rusty Shackleford says:
And your proof that the protesters are fake/paid is? Are you and your ilk so thin-skinned that because people don't agree with your point of view you instantly try to discredit them? It's funny that you romanticize and long for the American democracy of the 1940s, when African-Americans couldn't vote and we imprisoned Japanese-Americans. Really great times. We're well on our way to making America that great again. Keep up the good work.
Lt. Dan says:
And as a typical liberal, you attack anyone who has a different opinion. "It's funny that you romanticize and long for the American democracy of the 1940s, when African-Americans couldn't vote and we imprisoned Japanese-Americans. Really great times. " How you got that from this gentleman's article is beyond me.
Great insight into these circus acts that have recently played out all over the country.
So let me get this straight, by going to a town hall meeting and expressing yourself you are destroying your right to go to a town hall meeting to express yourself. Unfortunately people are dumb enough to believe this.
Regular Voter says:
No, Rick, by going to a town hall meeting and denying the speaker an opportunity to speak is what we are talking about. Yes, the opponents of the speaker have a right to be heard. Doesn't the speaker? Do the opponents have a right to deny the speaker HIS or HER right? That's what happening.
R B says:
These leftist agitators need some rightist counter agitators to instill an attitude adjustment that will trerminate such negative waves in an effective manner, ASAP !
Forming up some brown shirts, huh? Sounds like a good idea.
Citizens who drown out the speaker are not there to exchange ideas, they are there to shut down opposing ideas.
Greg Marr says:
We all know that this is exactly what is happening, but I've never heard it put so cogently. Bravo, and thank you!
How ironic that the leftist agitators are destroying an American hallmark, the town hall forum. By abusing their constitutional, democratic right to protest and to be heard by their elected public servants, they are shutting down meaningful discourse. They are cheating themselves from any meaningful exchange. They are not being heard.
You know who is shutting down meaningful discourse? Our president and his administration. They are cheating us all of a meaningful exchange by masterfully whipping up a media circus with outrageous tweets and bald-faced lies to distract from the scrutiny they face on issues.
We were talking about town hall meetings, not Donald Trump.