Merge pull request #10825 from edx/kill-psycho

Remove psychometrics app

common/lib/xmodule/xmodule/capa_base.py

@@ -1125,9 +1125,6 @@ class CapaMixin(CapaFields):
                 self.attempts,
             )
 
-        if hasattr(self.runtime, 'psychometrics_handler'):  # update PsychometricsData using callback
-            self.runtime.psychometrics_handler(self.get_state_for_lcp())
-
         # render problem into HTML
         html = self.get_problem_html(encapsulate=False)
 
@@ -1375,10 +1372,6 @@ class CapaMixin(CapaFields):
         event_info['attempts'] = self.attempts
         self.track_function_unmask('problem_rescore', event_info)
 
-        # psychometrics should be called on rescoring requests in the same way as check-problem
-        if hasattr(self.runtime, 'psychometrics_handler'):  # update PsychometricsData using callback
-            self.runtime.psychometrics_handler(self.get_state_for_lcp())
-
         return {'success': success}
 
     def save_problem(self, data):

common/test/db_cache/bok_choy_schema.sql

@@ -2528,20 +2528,6 @@ CREATE TABLE `programs_programsapiconfig` (
   CONSTRAINT `programs_programsa_changed_by_id_b7c3b49d5c0dcd3_fk_auth_user_id` FOREIGN KEY (`changed_by_id`) REFERENCES `auth_user` (`id`)
 ) ENGINE=InnoDB DEFAULT CHARSET=utf8;
 /*!40101 SET character_set_client = @saved_cs_client */;
-DROP TABLE IF EXISTS `psychometrics_psychometricdata`;
-/*!40101 SET @saved_cs_client     = @@character_set_client */;
-/*!40101 SET character_set_client = utf8 */;
-CREATE TABLE `psychometrics_psychometricdata` (
-  `id` int(11) NOT NULL AUTO_INCREMENT,
-  `done` tinyint(1) NOT NULL,
-  `attempts` int(11) NOT NULL,
-  `checktimes` longtext,
-  `studentmodule_id` int(11) NOT NULL,
-  PRIMARY KEY (`id`),
-  UNIQUE KEY `studentmodule_id` (`studentmodule_id`),
-  CONSTRAINT `D758b867e6fa9161734bd9cb58b9a485` FOREIGN KEY (`studentmodule_id`) REFERENCES `courseware_studentmodule` (`id`)
-) ENGINE=InnoDB DEFAULT CHARSET=utf8;
-/*!40101 SET character_set_client = @saved_cs_client */;
 DROP TABLE IF EXISTS `self_paced_selfpacedconfiguration`;
 /*!40101 SET @saved_cs_client     = @@character_set_client */;
 /*!40101 SET character_set_client = utf8 */;

lms/djangoapps/courseware/module_render.py

@@ -56,7 +56,6 @@ from openedx.core.lib.xblock_utils import (
     wrap_xblock,
     request_token as xblock_request_token,
 )
-from psychometrics.psychoanalyze import make_psychometrics_data_update_handler
 from student.models import anonymous_id_for_user, user_by_anonymous_id
 from student.roles import CourseBetaTesterRole
 from xblock.core import XBlock
@@ -760,11 +759,6 @@ def get_module_system_for_user(user, student_data,  # TODO  # pylint: disable=to
             position = None
 
     system.set('position', position)
-    if settings.FEATURES.get('ENABLE_PSYCHOMETRICS') and user.is_authenticated():
-        system.set(
-            'psychometrics_handler',  # set callback for updating PsychometricsData
-            make_psychometrics_data_update_handler(course_id, user, descriptor.location)
-        )
 
     system.set(u'user_is_staff', user_is_staff)
     system.set(u'user_is_admin', bool(has_access(user, u'staff', 'global')))

lms/djangoapps/courseware/tests/test_module_render.py

@@ -1767,17 +1767,6 @@ class TestRebindModule(TestSubmittingProblems):
         self.assertEqual(module.scope_ids.user_id, user2.id)
         self.assertEqual(module.descriptor.scope_ids.user_id, user2.id)
 
-    @patch('courseware.module_render.make_psychometrics_data_update_handler')
-    @patch.dict(settings.FEATURES, {'ENABLE_PSYCHOMETRICS': True})
-    def test_psychometrics_anonymous(self, psycho_handler):
-        """
-        Make sure that noauth modules with anonymous users don't have
-        the psychometrics callback bound.
-        """
-        module = self.get_module_for_user(self.anon_user)
-        module.system.rebind_noauth_module_to_user(module, self.anon_user)
-        self.assertFalse(psycho_handler.called)
-
 
 @attr('shard_1')
 @ddt.ddt

lms/djangoapps/instructor/views/legacy.py

@@ -48,7 +48,6 @@ from instructor_task.api import (
 from instructor_task.views import get_task_completion_info
 from edxmako.shortcuts import render_to_response, render_to_string
 from class_dashboard import dashboard_data
-from psychometrics import psychoanalyze
 from student.models import (
     CourseEnrollment,
     CourseEnrollmentAllowed,
@@ -97,7 +96,7 @@ def instructor_dashboard(request, course_id):
     plots = []
     datatable = {}
 
-    # the instructor dashboard page is modal: grades, psychometrics, admin
+    # the instructor dashboard page is modal: grades, admin
     # keep that state in request.session (defaults to grades mode)
     idash_mode = request.POST.get('idash_mode', '')
     idash_mode_key = u'idash_mode:{0}'.format(course_id)
@@ -320,18 +319,6 @@ def instructor_dashboard(request, course_id):
             datatable = ret['datatable']
 
     #----------------------------------------
-    # psychometrics
-
-    elif action == 'Generate Histogram and IRT Plot':
-        problem = request.POST['Problem']
-        nmsg, plots = psychoanalyze.generate_plots_for_problem(problem)
-        msg += nmsg
-        track.views.server_track(request, "psychometrics-histogram-generation", {"problem": unicode(problem)}, page="idashboard")
-
-    if idash_mode == 'Psychometrics':
-        problems = psychoanalyze.problems_with_psychometric_data(course_key)
-
-    #----------------------------------------
     # analytics
     def get_analytics_result(analytics_name):
         """Return data for an Analytic piece, or None if it doesn't exist. It
@@ -435,8 +422,6 @@ def instructor_dashboard(request, course_id):
 
         'show_email_tab': show_email_tab,  # email
 
-        'problems': problems,  # psychometrics
-        'plots': plots,  # psychometrics
         'course_errors': modulestore().get_course_errors(course.id),
         'instructor_tasks': instructor_tasks,
         'offline_grade_log': offline_grades_available(course_key),

lms/djangoapps/psychometrics/admin.py

-'''
-django admin pages for courseware model
-'''
-
-from psychometrics.models import PsychometricData
-from django.contrib import admin
-
-admin.site.register(PsychometricData)

lms/djangoapps/psychometrics/management/commands/init_psychometrics.py

-#!/usr/bin/python
-#
-# generate pyschometrics data from tracking logs and student module data
-
-import json
-
-from courseware.models import StudentModule
-from track.models import TrackingLog
-from psychometrics.models import PsychometricData
-
-from django.conf import settings
-from django.core.management.base import BaseCommand
-
-#db = "ocwtutor"	# for debugging
-#db = "default"
-
-db = getattr(settings, 'DATABASE_FOR_PSYCHOMETRICS', 'default')
-
-
-class Command(BaseCommand):
-    help = "initialize PsychometricData tables from StudentModule instances (and tracking data, if in SQL)."
-    help += "Note this is done for all courses for which StudentModule instances exist."
-
-    def handle(self, *args, **options):
-
-        # delete all pmd
-
-        #PsychometricData.objects.all().delete()
-        #PsychometricData.objects.using(db).all().delete()
-
-        smset = StudentModule.objects.using(db).exclude(max_grade=None)
-
-        for sm in smset:
-            usage_key = sm.module_state_key
-            if not usage_key.block_type == "problem":
-                continue
-            try:
-                state = json.loads(sm.state)
-                done = state['done']
-            except:
-                print "Oops, failed to eval state for %s (state=%s)" % (sm, sm.state)
-                continue
-
-            if done:			# only keep if problem completed
-                try:
-                    pmd = PsychometricData.objects.using(db).get(studentmodule=sm)
-                except PsychometricData.DoesNotExist:
-                    pmd = PsychometricData(studentmodule=sm)
-
-                pmd.done = done
-                pmd.attempts = state['attempts']
-
-                # get attempt times from tracking log
-                uname = sm.student.username
-                tset = TrackingLog.objects.using(db).filter(username=uname, event_type__contains='problem_check')
-                tset = tset.filter(event_source='server')
-                tset = tset.filter(event__contains="'%s'" % usage_key)
-                checktimes = [x.dtcreated for x in tset]
-                pmd.checktimes = checktimes
-                if not len(checktimes) == pmd.attempts:
-                    print "Oops, mismatch in number of attempts and check times for %s" % pmd
-
-                #print pmd
-                pmd.save(using=db)
-
-        print "%d PMD entries" % PsychometricData.objects.using(db).all().count()

lms/djangoapps/psychometrics/migrations/0001_initial.py

-# -*- coding: utf-8 -*-
-from __future__ import unicode_literals
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ('courseware', '0001_initial'),
-    ]
-
-    operations = [
-        migrations.CreateModel(
-            name='PsychometricData',
-            fields=[
-                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
-                ('done', models.BooleanField(default=False)),
-                ('attempts', models.IntegerField(default=0)),
-                ('checktimes', models.TextField(null=True, blank=True)),
-                ('studentmodule', models.OneToOneField(to='courseware.StudentModule')),
-            ],
-        ),
-    ]

lms/djangoapps/psychometrics/models.py

-#
-# db model for psychometrics data
-#
-# this data is collected in real time
-#
-
-from django.db import models
-from courseware.models import StudentModule
-
-
-class PsychometricData(models.Model):
-    """
-    This data is a table linking student, module, and module performance,
-    including number of attempts, grade, max grade, and time of checks.
-
-    Links to instances of StudentModule, but only those for capa problems.
-
-    Note that StudentModule.module_state_key is a :class:`Location` instance.
-
-    checktimes is extracted from tracking logs, or added by capa module via psychometrics callback.
-    """
-
-    class Meta(object):
-        app_label = "psychometrics"
-
-    studentmodule = models.OneToOneField(StudentModule, db_index=True)   # contains student, module_state_key, course_id
-
-    done = models.BooleanField(default=False)
-    attempts = models.IntegerField(default=0)			# extracted from studentmodule.state
-    checktimes = models.TextField(null=True, blank=True)  	# internally stored as list of datetime objects
-
-    # keep in mind
-    # grade = studentmodule.grade
-    # max_grade = studentmodule.max_grade
-    # student = studentmodule.student
-    # course_id = studentmodule.course_id
-    # location = studentmodule.module_state_key
-
-    def __unicode__(self):
-        sm = self.studentmodule
-        return "[PsychometricData] %s url=%s, grade=%s, max=%s, attempts=%s, ct=%s" % (sm.student,
-                                                                                       sm.module_state_key,
-                                                                                       sm.grade,
-                                                                                       sm.max_grade,
-                                                                                       self.attempts,
-                                                                                       self.checktimes)

lms/djangoapps/psychometrics/psychoanalyze.py

-#
-# File:   psychometrics/psychoanalyze.py
-#
-# generate pyschometrics plots from PsychometricData
-
-from __future__ import division
-
-import datetime
-import logging
-import json
-import math
-import numpy as np
-from opaque_keys.edx.locator import BlockUsageLocator
-from scipy.optimize import curve_fit
-
-from django.conf import settings
-from django.db.models import Sum, Max
-from psychometrics.models import PsychometricData
-from courseware.models import StudentModule
-from pytz import UTC
-
-log = logging.getLogger("edx.psychometrics")
-
-#db = "ocwtutor"        # for debugging
-#db = "default"
-
-db = getattr(settings, 'DATABASE_FOR_PSYCHOMETRICS', 'default')
-
-#-----------------------------------------------------------------------------
-# fit functions
-
-
-def func_2pl(x, a, b):
-    """
-    2-parameter logistic function
-    """
-    D = 1.7
-    edax = np.exp(D * a * (x - b))
-    return edax / (1 + edax)
-
-#-----------------------------------------------------------------------------
-# statistics class
-
-
-class StatVar(object):
-    """
-    Simple statistics on floating point numbers: avg, sdv, var, min, max
-    """
-    def __init__(self, unit=1):
-        self.sum = 0
-        self.sum2 = 0
-        self.cnt = 0
-        self.unit = unit
-        self.min = None
-        self.max = None
-
-    def add(self, x):
-        if x is None:
-            return
-        if self.min is None:
-            self.min = x
-        else:
-            if x < self.min:
-                self.min = x
-        if self.max is None:
-            self.max = x
-        else:
-            if x > self.max:
-                self.max = x
-        self.sum += x
-        self.sum2 += x ** 2
-        self.cnt += 1
-
-    def avg(self):
-        if self.cnt is None:
-            return 0
-        return self.sum / 1.0 / self.cnt / self.unit
-
-    def var(self):
-        if self.cnt is None:
-            return 0
-        return (self.sum2 / 1.0 / self.cnt / (self.unit ** 2)) - (self.avg() ** 2)
-
-    def sdv(self):
-        v = self.var()
-        if v > 0:
-            return math.sqrt(v)
-        else:
-            return 0
-
-    def __str__(self):
-        return 'cnt=%d, avg=%f, sdv=%f' % (self.cnt, self.avg(), self.sdv())
-
-    def __add__(self, x):
-        self.add(x)
-        return self
-
-#-----------------------------------------------------------------------------
-# histogram generator
-
-
-def make_histogram(ydata, bins=None):
-    '''
-    Generate histogram of ydata using bins provided, or by default bins
-    from 0 to 100 by 10.  bins should be ordered in increasing order.
-
-    returns dict with keys being bins, and values being counts.
-    special: hist['bins'] = bins
-    '''
-    if bins is None:
-        bins = range(0, 100, 10)
-
-    nbins = len(bins)
-    hist = dict(zip(bins, [0] * nbins))
-    for y in ydata:
-        for b in bins[::-1]:  # in reverse order
-            if y > b:
-                hist[b] += 1
-                break
-    # hist['bins'] = bins
-    return hist
-
-#-----------------------------------------------------------------------------
-
-
-def problems_with_psychometric_data(course_id):
-    '''
-    Return dict of {problems (location urls): count} for which psychometric data is available.
-    Does this for a given course_id.
-    '''
-    pmdset = PsychometricData.objects.using(db).filter(studentmodule__course_id=course_id)
-    plist = [p['studentmodule__module_state_key'] for p in pmdset.values('studentmodule__module_state_key').distinct()]
-    problems = dict(
-        (
-            p,
-            pmdset.filter(
-                studentmodule__module_state_key=BlockUsageLocator.from_string(p)
-            ).count()
-        ) for p in plist
-    )
-
-    return problems
-
-#-----------------------------------------------------------------------------
-
-
-def generate_plots_for_problem(problem):
-
-    pmdset = PsychometricData.objects.using(db).filter(
-        studentmodule__module_state_key=BlockUsageLocator.from_string(problem)
-    )
-    nstudents = pmdset.count()
-    msg = ""
-    plots = []
-
-    if nstudents < 2:
-        msg += "%s nstudents=%d --> skipping, too few" % (problem, nstudents)
-        return msg, plots
-
-    max_grade = pmdset[0].studentmodule.max_grade
-
-    agdat = pmdset.aggregate(Sum('attempts'), Max('attempts'))
-    max_attempts = agdat['attempts__max']
-    total_attempts = agdat['attempts__sum']  # not used yet
-
-    msg += "max attempts = %d" % max_attempts
-
-    xdat = range(1, max_attempts + 1)
-    dataset = {'xdat': xdat}
-
-    # compute grade statistics
-    grades = [pmd.studentmodule.grade for pmd in pmdset]
-    gsv = StatVar()
-    for g in grades:
-        gsv += g
-    msg += "<br><p><font color='blue'>Grade distribution: %s</font></p>" % gsv
-
-    # generate grade histogram
-    ghist = []
-
-    axisopts = """{
-        xaxes: [{
-            axisLabel: 'Grade'
-        }],
-        yaxes: [{
-            position: 'left',
-            axisLabel: 'Count'
-         }]
-         }"""
-
-    if gsv.max > max_grade:
-        msg += "<br/><p><font color='red'>Something is wrong: max_grade=%s, but max(grades)=%s</font></p>" % (max_grade, gsv.max)
-        max_grade = gsv.max
-
-    if max_grade > 1:
-        ghist = make_histogram(grades, np.linspace(0, max_grade, max_grade + 1))
-        ghist_json = json.dumps(ghist.items())
-
-        plot = {'title': "Grade histogram for %s" % problem,
-                'id': 'histogram',
-                'info': '',
-                'data': "var dhist = %s;\n" % ghist_json,
-                'cmd': '[ {data: dhist, bars: { show: true, align: "center" }} ], %s' % axisopts,
-                }
-        plots.append(plot)
-    else:
-        msg += "<br/>Not generating histogram: max_grade=%s" % max_grade
-
-    # histogram of time differences between checks
-    # Warning: this is inefficient - doesn't scale to large numbers of students
-    dtset = []  # time differences in minutes
-    dtsv = StatVar()
-    for pmd in pmdset:
-        try:
-            checktimes = eval(pmd.checktimes)  # update log of attempt timestamps
-        except:
-            continue
-        if len(checktimes) < 2:
-            continue
-        ct0 = checktimes[0]
-        for ct in checktimes[1:]:
-            dt = (ct - ct0).total_seconds() / 60.0
-            if dt < 20:  # ignore if dt too long
-                dtset.append(dt)
-                dtsv += dt
-            ct0 = ct
-    if dtsv.cnt > 2:
-        msg += "<br/><p><font color='brown'>Time differences between checks: %s</font></p>" % dtsv
-        bins = np.linspace(0, 1.5 * dtsv.sdv(), 30)
-        dbar = bins[1] - bins[0]
-        thist = make_histogram(dtset, bins)
-        thist_json = json.dumps(sorted(thist.items(), key=lambda(x): x[0]))
-
-        axisopts = """{ xaxes: [{ axisLabel: 'Time (min)'}], yaxes: [{position: 'left',axisLabel: 'Count'}]}"""
-
-        plot = {'title': "Histogram of time differences between checks",
-                'id': 'thistogram',
-                'info': '',
-                'data': "var thist = %s;\n" % thist_json,
-                'cmd': '[ {data: thist, bars: { show: true, align: "center", barWidth:%f }} ], %s' % (dbar, axisopts),
-                }
-        plots.append(plot)
-
-    # one IRT plot curve for each grade received (TODO: this assumes integer grades)
-    for grade in range(1, int(max_grade) + 1):
-        yset = {}
-        gset = pmdset.filter(studentmodule__grade=grade)
-        ngset = gset.count()
-        if ngset == 0:
-            continue
-        ydat = []
-        ylast = 0
-        for x in xdat:
-            y = gset.filter(attempts=x).count() / ngset
-            ydat.append(y + ylast)
-            ylast = y + ylast
-        yset['ydat'] = ydat
-
-        if len(ydat) > 3:  # try to fit to logistic function if enough data points
-            try:
-                cfp = curve_fit(func_2pl, xdat, ydat, [1.0, max_attempts / 2.0])
-                yset['fitparam'] = cfp
-                yset['fitpts'] = func_2pl(np.array(xdat), *cfp[0])
-                yset['fiterr'] = [yd - yf for (yd, yf) in zip(ydat, yset['fitpts'])]
-                fitx = np.linspace(xdat[0], xdat[-1], 100)
-                yset['fitx'] = fitx
-                yset['fity'] = func_2pl(np.array(fitx), *cfp[0])
-            except Exception as err:
-                log.debug('Error in psychoanalyze curve fitting: %s', err)
-
-        dataset['grade_%d' % grade] = yset
-
-    axisopts = """{
-        xaxes: [{
-            axisLabel: 'Number of Attempts'
-        }],
-        yaxes: [{
-            max:1.0,
-            position: 'left',
-            axisLabel: 'Probability of correctness'
-         }]
-         }"""
-
-    # generate points for flot plot
-    for grade in range(1, int(max_grade) + 1):
-        jsdata = ""
-        jsplots = []
-        gkey = 'grade_%d' % grade
-        if gkey in dataset:
-            yset = dataset[gkey]
-            jsdata += "var d%d = %s;\n" % (grade, json.dumps(zip(xdat, yset['ydat'])))
-            jsplots.append('{ data: d%d, lines: { show: false }, points: { show: true}, color: "red" }' % grade)
-            if 'fitpts' in yset:
-                jsdata += 'var fit = %s;\n' % (json.dumps(zip(yset['fitx'], yset['fity'])))
-                jsplots.append('{ data: fit,  lines: { show: true }, color: "blue" }')
-                (a, b) = yset['fitparam'][0]
-                irtinfo = "(2PL: D=1.7, a=%6.3f, b=%6.3f)" % (a, b)
-            else:
-                irtinfo = ""
-
-            plots.append({'title': 'IRT Plot for grade=%s %s' % (grade, irtinfo),
-                          'id': "irt%s" % grade,
-                          'info': '',
-                          'data': jsdata,
-                          'cmd': '[%s], %s' % (','.join(jsplots), axisopts),
-                          })
-
-    #log.debug('plots = %s' % plots)
-    return msg, plots
-
-#-----------------------------------------------------------------------------
-
-
-def make_psychometrics_data_update_handler(course_id, user, module_state_key):
-    """
-    Construct and return a procedure which may be called to update
-    the PsychometricData instance for the given StudentModule instance.
-    """
-    sm, status = StudentModule.objects.get_or_create(
-        course_id=course_id,
-        student=user,
-        module_state_key=module_state_key,
-        defaults={'state': '{}', 'module_type': 'problem'},
-    )
-
-    try:
-        pmd = PsychometricData.objects.using(db).get(studentmodule=sm)
-    except PsychometricData.DoesNotExist:
-        pmd = PsychometricData(studentmodule=sm)
-
-    def psychometrics_data_update_handler(state):
-        """
-        This function may be called each time a problem is successfully checked
-        (eg on save_problem_check events in capa_module).
-
-        state = instance state (a nice, uniform way to interface - for more future psychometric feature extraction)
-        """
-        try:
-            state = json.loads(sm.state)
-            done = state['done']
-        except:
-            log.exception("Oops, failed to eval state for %s (state=%s)", sm, sm.state)
-            return
-
-        pmd.done = done
-        try:
-            pmd.attempts = state.get('attempts', 0)
-        except:
-            log.exception("no attempts for %s (state=%s)", sm, sm.state)
-
-        try:
-            checktimes = eval(pmd.checktimes)  # update log of attempt timestamps
-        except:
-            checktimes = []
-        checktimes.append(datetime.datetime.now(UTC))
-        pmd.checktimes = checktimes
-        try:
-            pmd.save()
-        except:
-            log.exception("Error in updating psychometrics data for %s", sm)
-
-    return psychometrics_data_update_handler

lms/envs/common.py

@@ -116,8 +116,6 @@ FEATURES = {
     # in their emails, and they will have no way to resubscribe.
     'ENABLE_DISCUSSION_EMAIL_DIGEST': False,
 
-    'ENABLE_PSYCHOMETRICS': False,  # real-time psychometrics (eg item response theory analysis in instructor dashboard)
-
     'ENABLE_DJANGO_ADMIN_SITE': True,  # set true to enable django's admin site, even on prod (e.g. for course ops)
     'ENABLE_SQL_TRACKING_LOGS': False,
     'ENABLE_LMS_MIGRATION': False,
@@ -1833,7 +1831,6 @@ INSTALLED_APPS = (
     'instructor',
     'instructor_task',
     'open_ended_grading',
-    'psychometrics',
     'licenses',
     'openedx.core.djangoapps.course_groups',
     'bulk_email',

lms/envs/dev.py

@@ -24,7 +24,6 @@ FEATURES['SUBDOMAIN_COURSE_LISTINGS'] = False  # Enable to test subdomains--othe
 FEATURES['SUBDOMAIN_BRANDING'] = True
 FEATURES['FORCE_UNIVERSITY_DOMAIN'] = None		# show all university courses if in dev (ie don't use HTTP_HOST)
 FEATURES['ENABLE_MANUAL_GIT_RELOAD'] = True
-FEATURES['ENABLE_PSYCHOMETRICS'] = False    # real-time psychometrics (eg item response theory analysis in instructor dashboard)
 FEATURES['ENABLE_SERVICE_STATUS'] = True
 FEATURES['ENABLE_INSTRUCTOR_EMAIL'] = True     # Enable email for all Studio courses
 FEATURES['REQUIRE_COURSE_EMAIL_AUTH'] = False  # Give all courses email (don't require django-admin perms)

lms/templates/courseware/legacy_instructor_dashboard.html

@@ -148,9 +148,6 @@ function goto( mode)
     %endif
 
     <h2 class="navbar">[ <a href="#" onclick="goto('Grades');" class="${modeflag.get('Grades')}">Grades</a> |
-          %if settings.FEATURES.get('ENABLE_PSYCHOMETRICS'):
-            <a href="#" onclick="goto('Psychometrics');" class="${modeflag.get('Psychometrics')}">${_("Psychometrics")}</a> |
-          %endif
           <a href="#" onclick="goto('Admin');" class="${modeflag.get('Admin')}">${_("Admin")}</a> |
           <a href="#" onclick="goto('Forum Admin');" class="${modeflag.get('Forum Admin')}">${_("Forum Admin")}</a> |
           <a href="#" onclick="goto('Enrollment');" class="${modeflag.get('Enrollment')}">${_("Enrollment")}</a> |
@@ -268,27 +265,6 @@ function goto( mode)
  %endif
 
 ##-----------------------------------------------------------------------------
-%if modeflag.get('Psychometrics'):
-
-    <p>${_("Select a problem and an action:")}
-    </p>
-
-    <p>
-    <select name="Problem">
-    %for problem, count in sorted(problems.items(), key=lambda x: x[0]):
-        <option value="${problem}">${problem} [${count}]</option>
-    %endfor
-    </select>
-    </p>
-    <p>
-    <input type="submit" name="action" value="Generate Histogram and IRT Plot">
-    </p>
-
-    <p></p>
-
-%endif
-
-##-----------------------------------------------------------------------------
 %if modeflag.get('Admin'):
 
   %if instructor_access or admin_access:
@@ -398,7 +374,7 @@ function goto( mode)
 
 ##-----------------------------------------------------------------------------
 
-%if datatable and modeflag.get('Psychometrics') is None:
+%if datatable:
 
     <br/>
     <br/>
@@ -492,32 +468,6 @@ function goto( mode)
 %endif
 
 ##-----------------------------------------------------------------------------
-%if modeflag.get('Psychometrics'):
-
-    %for plot in plots:
-      <br/>
-      <h3>${plot['title']}</h3>
-      <br/>
-      <p>${plot['info']}</p>
-      <br/>
-      <div id="plot_${plot['id']}" style="width:600px;height:300px;"></div>
-      <script type="text/javascript">
-          $(function () {
-              ${plot['data']}
-              $.plot($("#plot_${plot['id']}"), ${plot['cmd']}  );
-          });
-      </script>
-      <br/>
-      <br/>
-    %endfor
-
-%endif
-
-##-----------------------------------------------------------------------------
-## always show msg
-
-
-##-----------------------------------------------------------------------------
 %if modeflag.get('Admin'):
  % if course_errors is not UNDEFINED:
     <h2>${_("Course errors")}</h2>